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Sample records for repetitive spike patterns

  1. Self-organization of repetitive spike patterns in developing neuronal networks in vitro.

    Science.gov (United States)

    Sun, Jyh-Jang; Kilb, Werner; Luhmann, Heiko J

    2010-10-01

    The appearance of spontaneous correlated activity is a fundamental feature of developing neuronal networks in vivo and in vitro. To elucidate whether the ontogeny of correlated activity is paralleled by the appearance of specific spike patterns we used a template-matching algorithm to detect repetitive spike patterns in multi-electrode array recordings from cultures of dissociated mouse neocortical neurons between 6 and 15 days in vitro (div). These experiments demonstrated that the number of spiking neurons increased significantly between 6 and 15 div, while a significantly synchronized network activity appeared at 9 div and became the main discharge pattern in the subsequent div. Repetitive spike patterns with a low complexity were first observed at 8 div. The number of repetitive spike patterns in each dataset as well as their complexity and recurrence increased during development in vitro. The number of links between neurons implicated in repetitive spike patterns, as well as their strength, showed a gradual increase during development. About 8% of the spike sequences contributed to more than one repetitive spike patterns and were classified as core patterns. These results demonstrate for the first time that defined neuronal assemblies, as represented by repetitive spike patterns, appear quite early during development in vitro, around the time synchronized network burst become the dominant network pattern. In summary, these findings suggest that dissociated neurons can self-organize into complex neuronal networks that allow reliable flow and processing of neuronal information already during early phases of development.

  2. Varianish: Jamming with Pattern Repetition

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    Jort Band

    2014-10-01

    Full Text Available In music, patterns and pattern repetition are often regarded as a machine-like task, indeed often delegated to drum Machines and sequencers. Nevertheless, human players add subtle differences and variations to repeated patterns that are musically interesting and often unique. Especially when looking at minimal music, pattern repetitions create hypnotic effects and the human mind blends out the actual pattern to focus on variation and tiny differences over time. Varianish is a musical instrument that aims at turning this phenomenon into a new musical experience for musician and audience: Musical pattern repetitions are found in live music and Varianish generates additional (musical output accordingly that adds substantially to the overall musical expression. Apart from the theory behind the pattern finding and matching and the conceptual design, a demonstrator implementation of Varianish is presented and evaluated.

  3. Spiking Neurons for Analysis of Patterns

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    Huntsberger, Terrance

    2008-01-01

    Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

  4. Light-dependent repetitive Ca2+ spikes induced by extracellular application of neomycin in honeybee drone photoreceptors.

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    Walz, B; Zimmermann, B; Ukhanov, K

    2000-05-01

    Photoreceptor cells of the honeybee drone fire, in the presence of the polycationic aminoglycoside neomycin, repetitive slow spike-like potentials superimposed on the receptor potential plateau phase. We have used conventional intracellular recordings and microfluorometric intracellular Ca2+ measurements to characterize these spike potentials. We have shown that the spike frequency increases in a light-intensity-dependent manner. The spikes are fired only when light stimuli depolarize the cell from a resting potential of -50 to -60 mV to at least -40 to -45 mV; they are tetrodotoxin insensitive and blocked by the Ca2+ channel blockers Ni2+, Cd2+, omega-agatoxin TK, verapamil and methoxyverapamil. Depolarization of the photoreceptors with high extracellular K+ in the presence of neomycin in darkness does not generate spikes. Small intracellular Ca2+ oscillations superimposed on the plateau phase of the light-induced increase in intracellular free Ca2+ concentration have a similar temporal pattern as the spike-like potentials. We conclude that the spike-like potentials require stimulation by light and are generated by voltage-dependent Ca2+ channels localized on the soma of the photoreceptors, distal to the basal lamina.

  5. The Mechanisms of Repetitive Spike Generation in an Axonless Retinal Interneuron

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    Mark S. Cembrowski

    2012-02-01

    Full Text Available Several types of retinal interneurons exhibit spikes but lack axons. One such neuron is the AII amacrine cell, in which spikes recorded at the soma exhibit small amplitudes (5 ms. Here, we used electrophysiological recordings and computational analysis to examine the mechanisms underlying this atypical spiking. We found that somatic spikes likely represent large, brief action potential-like events initiated in a single, electrotonically distal dendritic compartment. In this same compartment, spiking undergoes slow modulation, likely by an M-type K conductance. The structural correlate of this compartment is a thin neurite that extends from the primary dendritic tree: local application of TTX to this neurite, or excision of it, eliminates spiking. Thus, the physiology of the axonless AII is much more complex than would be anticipated from morphological descriptions and somatic recordings; in particular, the AII possesses a single dendritic structure that controls its firing pattern.

  6. Motor control by precisely timed spike patterns

    DEFF Research Database (Denmark)

    Srivastava, Kyle H; Holmes, Caroline M; Vellema, Michiel

    2017-01-01

    A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time...

  7. Weak noise in neurons may powerfully inhibit the generation of repetitive spiking but not its propagation.

    Science.gov (United States)

    Tuckwell, Henry C; Jost, Jürgen

    2010-05-27

    Many neurons have epochs in which they fire action potentials in an approximately periodic fashion. To see what effects noise of relatively small amplitude has on such repetitive activity we recently examined the response of the Hodgkin-Huxley (HH) space-clamped system to such noise as the mean and variance of the applied current vary, near the bifurcation to periodic firing. This article is concerned with a more realistic neuron model which includes spatial extent. Employing the Hodgkin-Huxley partial differential equation system, the deterministic component of the input current is restricted to a small segment whereas the stochastic component extends over a region which may or may not overlap the deterministic component. For mean values below, near and above the critical values for repetitive spiking, the effects of weak noise of increasing strength is ascertained by simulation. As in the point model, small amplitude noise near the critical value dampens the spiking activity and leads to a minimum as noise level increases. This was the case for both additive noise and conductance-based noise. Uniform noise along the whole neuron is only marginally more effective in silencing the cell than noise which occurs near the region of excitation. In fact it is found that if signal and noise overlap in spatial extent, then weak noise may inhibit spiking. If, however, signal and noise are applied on disjoint intervals, then the noise has no effect on the spiking activity, no matter how large its region of application, though the trajectories are naturally altered slightly by noise. Such effects could not be discerned in a point model and are important for real neuron behavior. Interference with the spike train does nevertheless occur when the noise amplitude is larger, even when noise and signal do not overlap, being due to the instigation of secondary noise-induced wave phenomena rather than switching the system from one attractor (firing regularly) to another (a stable

  8. Weak noise in neurons may powerfully inhibit the generation of repetitive spiking but not its propagation.

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    Henry C Tuckwell

    2010-05-01

    Full Text Available Many neurons have epochs in which they fire action potentials in an approximately periodic fashion. To see what effects noise of relatively small amplitude has on such repetitive activity we recently examined the response of the Hodgkin-Huxley (HH space-clamped system to such noise as the mean and variance of the applied current vary, near the bifurcation to periodic firing. This article is concerned with a more realistic neuron model which includes spatial extent. Employing the Hodgkin-Huxley partial differential equation system, the deterministic component of the input current is restricted to a small segment whereas the stochastic component extends over a region which may or may not overlap the deterministic component. For mean values below, near and above the critical values for repetitive spiking, the effects of weak noise of increasing strength is ascertained by simulation. As in the point model, small amplitude noise near the critical value dampens the spiking activity and leads to a minimum as noise level increases. This was the case for both additive noise and conductance-based noise. Uniform noise along the whole neuron is only marginally more effective in silencing the cell than noise which occurs near the region of excitation. In fact it is found that if signal and noise overlap in spatial extent, then weak noise may inhibit spiking. If, however, signal and noise are applied on disjoint intervals, then the noise has no effect on the spiking activity, no matter how large its region of application, though the trajectories are naturally altered slightly by noise. Such effects could not be discerned in a point model and are important for real neuron behavior. Interference with the spike train does nevertheless occur when the noise amplitude is larger, even when noise and signal do not overlap, being due to the instigation of secondary noise-induced wave phenomena rather than switching the system from one attractor (firing regularly to

  9. A model of reverse spike frequency adaptation and repetitive firing of subthalamic nucleus neurons.

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    Wilson, Charles J; Weyrick, Angela; Terman, David; Hallworth, Nicholas E; Bevan, Mark D

    2004-05-01

    Subthalamic nucleus neurons exhibit reverse spike-frequency adaptation. This occurs only at firing rates of 20-50 spikes/s and higher. Over this same frequency range, there is an increase in the steady-state frequency-intensity (F-I) curve's slope (the secondary range). Specific blockade of high-voltage activated calcium currents reduced the F-I curve slope and reverse adaptation. Blockade of calcium-dependent potassium current enhanced secondary range firing. A simple model that exhibited these properties used spike-triggered conductances similar to those in subthalamic neurons. It showed: 1) Nonaccumulating spike afterhyperpolarizations produce positively accelerating F-I curves and spike-frequency adaptation that is complete after the second spike. 2) Combinations of accumulating aftercurrents result in a linear F-I curve, whose slope depends on the relative contributions of inward and outward currents. Spike-frequency adaptation can be gradual. 3) With both accumulating and nonaccumulating aftercurrents, primary and secondary ranges will be present in the F-I curve. The slope of the primary range is determined by the nonaccumulating conductance; the accumulating conductances govern the secondary range. The transition is determined by the relative strengths of accumulating and nonaccumulating currents. 4) Spike-threshold accommodation contributes to the secondary range, reducing its slope at high firing rates. Threshold accommodation can stabilize firing when inward aftercurrents exceed outward ones. 5) Steady-state reverse adaptation results when accumulated inward aftercurrents exceed outward ones. This requires spike-threshold accommodation. Transient speedup arises when inward currents are smaller than outward ones at steady state, but accumulate more rapidly. 6) The same mechanisms alter firing in response to irregular patterns of synaptic conductances, as cell excitability fluctuates with changes in firing rate.

  10. Stochastically gating ion channels enable patterned spike firing through activity-dependent modulation of spike probability.

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    Joshua T Dudman

    2009-02-01

    Full Text Available The transformation of synaptic input into patterns of spike output is a fundamental operation that is determined by the particular complement of ion channels that a neuron expresses. Although it is well established that individual ion channel proteins make stochastic transitions between conducting and non-conducting states, most models of synaptic integration are deterministic, and relatively little is known about the functional consequences of interactions between stochastically gating ion channels. Here, we show that a model of stellate neurons from layer II of the medial entorhinal cortex implemented with either stochastic or deterministically gating ion channels can reproduce the resting membrane properties of stellate neurons, but only the stochastic version of the model can fully account for perithreshold membrane potential fluctuations and clustered patterns of spike output that are recorded from stellate neurons during depolarized states. We demonstrate that the stochastic model implements an example of a general mechanism for patterning of neuronal output through activity-dependent changes in the probability of spike firing. Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization. Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization. Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns.

  11. Temporal pattern recognition based on instantaneous spike rate coding in a simple auditory system.

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    Nabatiyan, A; Poulet, J F A; de Polavieja, G G; Hedwig, B

    2003-10-01

    Auditory pattern recognition by the CNS is a fundamental process in acoustic communication. Because crickets communicate with stereotyped patterns of constant frequency syllables, they are established models to investigate the neuronal mechanisms of auditory pattern recognition. Here we provide evidence that for the neural processing of amplitude-modulated sounds, the instantaneous spike rate rather than the time-averaged neural activity is the appropriate coding principle by comparing both coding parameters in a thoracic interneuron (Omega neuron ON1) of the cricket (Gryllus bimaculatus) auditory system. When stimulated with different temporal sound patterns, the analysis of the instantaneous spike rate demonstrates that the neuron acts as a low-pass filter for syllable patterns. The instantaneous spike rate is low at high syllable rates, but prominent peaks in the instantaneous spike rate are generated as the syllable rate resembles that of the species-specific pattern. The occurrence and repetition rate of these peaks in the neuronal discharge are sufficient to explain temporal filtering in the cricket auditory pathway as they closely match the tuning of phonotactic behavior to different sound patterns. Thus temporal filtering or "pattern recognition" occurs at an early stage in the auditory pathway.

  12. Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains.

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    Timothée Masquelier

    Full Text Available Experimental studies have observed Long Term synaptic Potentiation (LTP when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependent Plasticity (STDP. When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn 'early spike patterns', that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense 'distractor' spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it.

  13. Inferring Synaptic Connectivity from Spatio-Temporal Spike Patterns

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    Frank eVan Bussel

    2011-02-01

    Full Text Available Networks of well-known dynamical units but unknown interaction topology arise across various fields of biology, including genetics, ecology and neuroscience. The collective dynamics of such networks is often sensitive to the presence (or absence of individual interactions, but there is usually no direct way to probe for their existence. Here we present an explicit method for reconstructing interaction networks of leaky integrate-and-fire neurons from the spike patterns they exhibit in response to external driving. Given the dynamical parameters are known, the approach works well for networks in simple collective states but is also applicable to networks exhibiting complex spatio-temporal spike patterns. In particular, stationarity of spiking time series is not required.

  14. Learning and Prospective Recall of Noisy Spike Pattern Episodes

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    Karl eDockendorf

    2013-06-01

    Full Text Available Spike patterns in vivo are often incomplete or corrupted with noise that makes inputs to neuronal networks appear to vary although they may, in fact, be samples of a single underlying pattern or repeated presentation. Here we present a recurrent spiking neural network (SNN model that learns noisy pattern sequences through the use of homeostasis and spike-timing dependent plasticity (STDP. We find that the changes in the synaptic weight vector during learning of patterns of random ensembles are approximately orthogonal in a reduced dimension space when the patterns are constructed to minimize overlap in representations. Using this model, representations of sparse patterns maybe associated through co-activated firing and integrated into ensemble representations. While the model is tolerant to noise, prospective activity and pattern completion differ in their ability to adapt in the presence of noise. One version of the model is able to demonstrate the recently discovered phenomena of preplay and replay reminiscent of hippocampal like behaviors.

  15. Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

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    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

    Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

  16. Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

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    Bi, Zedong; Zhou, Changsong

    2016-01-01

    Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy). PMID:27555816

  17. Recurrent coupling improves discrimination of temporal spike patterns

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    Chun-Wei eYuan

    2012-05-01

    Full Text Available Despite the ubiquitous presence of recurrent synaptic connections insensory neuronal systems, their general functional purpose is not wellunderstood. A recent conceptual advance has been achieved by theoriesof reservoir computing in which recurrent networks have been proposedto generate short-term memory as well as to improve neuronalrepresentation of the sensory input for subsequent computations.Here, we present a numerical study on the distinct effects ofinhibitory and excitatory recurrence in a canonical linearclassification task. It is found that both types of coupling improvethe ability to discriminate temporal spike patterns as compared to apurely feed-forward system, although in different ways. For a largeclass of inhibitory networks, the network's performance is optimal aslong as a fraction of roughly 50% of neurons per stimulus is activein the resulting population code. Thereby the contribution of inactiveneurons to the neural code is found to be even more informative thanthat of the active neurons, generating an inherent robustness ofclassification performance against temporal jitter of the inputspikes. Excitatory couplings are found to not only produce ashort-term memory buffer but also to improve linear separability ofthe population patterns by evoking more irregular firing as comparedto the purely inhibitory case. As the excitatory connectivity becomesmore sparse, firing becomes more variable and pattern separabilityimproves. We argue that the proposed paradigm is particularlywell-suited as a conceptual framework for processing of sensoryinformation in the auditory pathway.

  18. Scan patterns when viewing natural scenes: emotion, complexity, and repetition.

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    Bradley, Margaret M; Houbova, Petra; Miccoli, Laura; Costa, Vincent D; Lang, Peter J

    2011-11-01

    Eye movements were monitored during picture viewing, and effects of hedonic content, perceptual composition, and repetition on scanning assessed. In Experiment 1, emotional and neutral pictures that were figure-ground compositions or more complex scenes were presented for a 6-s free viewing period. Viewing emotional pictures or complex scenes prompted more fixations and broader scanning of the visual array, compared to neutral pictures or simple figure-ground compositions. Effects of emotion and composition were independent, supporting the hypothesis that these oculomotor indices reflect enhanced information seeking. Experiment 2 tested an orienting hypothesis by repeatedly presenting the same pictures. Although repetition altered specific scan patterns, emotional, compared to neutral, picture viewing continued to prompt oculomotor differences, suggesting that motivationally relevant cues enhance information seeking in appetitive and defensive contexts. Copyright © 2011 Society for Psychophysiological Research.

  19. Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems

    Energy Technology Data Exchange (ETDEWEB)

    Hurtado, Antonio, E-mail: antonio.hurtado@strath.ac.uk [Institute of Photonics, SUPA Department of Physics, University of Strathclyde, TIC Centre, 99 George Street, Glasgow G1 1RD (United Kingdom); Javaloyes, Julien [Departament de Fisica, Universitat de les Illes Balears, c/Valldemossa km 7.5, 07122 Mallorca (Spain)

    2015-12-14

    Multiple controllable spiking patterns are achieved in a 1310 nm Vertical-Cavity Surface Emitting Laser (VCSEL) in response to induced perturbations and for two different cases of polarized optical injection, namely, parallel and orthogonal. Furthermore, reproducible spiking responses are demonstrated experimentally at sub-nanosecond speed resolution and with a controlled number of spikes fired. This work opens therefore exciting research avenues for the use of VCSELs in ultrafast neuromorphic photonic systems for non-traditional computing applications, such as all-optical binary-to-spiking format conversion and spiking information encoding.

  20. Wicket spikes: clinical correlates of a previously undescribed EEG pattern.

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    Reiher, J; Lebel, M

    1977-02-01

    From an analysis of the electroencephalograms of 4,458 patients who underwent recording during both wakefulness ans sleep, through the years 1969 to 1975, wicket spikes-- recorded in 39 patients-- may be described as follows: They were found during both wakefulness ans sleep, almost exclusively in adults. Their cardinal feature is a changing mode of occurrence through any single recording: from intermittent trains of more or less sustained, arciform, discharges resembling mu rhythm, to sporadic, urinary, single spikes. When occurring singly, wicket spikes can be mistaken for anterior or middle temporal spikes, since they predominate in eith er area, and since they share with them other characteristics such as amplitude (60 to 210 microvolts), polarity (surface negative) duration, and configuration. Wicket spikes should not be considered interictal abnormalities; they do nor correlate with epilepsy or with any particular symptom complex.

  1. Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

    OpenAIRE

    Wu, Xinyu; Saxena, Vishal; Zhu, Kehan

    2015-01-01

    A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable...

  2. Extracting information in spike time patterns with wavelets and information theory.

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    Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo

    2015-02-01

    We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. Copyright © 2015 the American Physiological Society.

  3. Spiking patterns of neocortical L5 pyramidal neurons in vitro change with temperature

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    Tristan eHedrick

    2011-01-01

    Full Text Available A subset of pyramidal neurons in layer 5 of the mammalian neocortex can fire action potentials in brief, high-frequency bursts while others fire spikes at regularly-spaced intervals. Here we show that individual layer 5 pyramidal neurons in acute slices from mouse primary motor cortex can adopt both regular and burst spiking patterns. During constant current injection at the soma, neurons displayed a regular firing pattern at 36-37 °C, but switched to burst spiking patterns upon cooling the slice to 24-26 °C. This change in firing pattern was reversible and repeatable and was independent of the somatic resting membrane potential. Hence these spiking patterns are not inherent to discrete populations of pyramidal neurons and are more interchangeable than previously thought. Burst spiking in these neurons is the result of electrical interactions between the soma and distal apical dendritic tree. Presumably the interactions between soma and distal dendrite are temperature-sensitive, suggesting that the manner in which layer 5 pyramidal neurons translate synaptic input into an output spiking pattern is fundamentally altered at sub-physiological temperatures.

  4. Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI.

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    Mitra, S; Fusi, S; Indiveri, G

    2009-02-01

    Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated.

  5. Systematic regional variations in Purkinje cell spiking patterns.

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    Jianqiang Xiao

    Full Text Available In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+ and negative (Z- bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z- PCs, respectively. In both datasets significant differences in simple spike (SS activity were observed between cortical regions. Specifically, Z- and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions.

  6. Conditional probability based significance tests for sequential patterns in multi-neuronal spike trains

    CERN Document Server

    Sastry, P S

    2008-01-01

    In this paper we consider the problem of detecting statistically significant sequential patterns in multi-neuronal spike trains. These patterns are characterized by an ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data mining scheme to efficiently discover such patterns which are frequent in the sense that the count of non-overlapping occurrences of the pattern in the data stream is above a threshold. Here we propose a method to determine the statistical significance of these repeating patterns and to set the thresholds automatically. The novelty of our approach is that we use a compound null hypothesis that includes not only models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a proba...

  7. iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains.

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    Somerville, J; Stuart, L; Sernagor, E; Borisyuk, R

    2010-12-15

    Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis).

  8. The chronotron: a neuron that learns to fire temporally precise spike patterns.

    Directory of Open Access Journals (Sweden)

    Răzvan V Florian

    Full Text Available In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons, one that provides high memory capacity (E-learning, and one that has a higher biological plausibility (I-learning. With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.

  9. Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

    Science.gov (United States)

    Vanleer, Ann C.; Blanco, Justin A.; Wagenaar, Joost B.; Viventi, Jonathan; Contreras, Diego; Litt, Brian

    2016-04-01

    Objective. Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential (LFP) spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. Approach. We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from LFP spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two-dimensional spike patterns during seizures were different from those between seizures. Main results. We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. Significance. We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state.

  10. What can neuromorphic event-driven precise timing add to spike-based pattern recognition?

    Science.gov (United States)

    Akolkar, Himanshu; Meyer, Cedric; Clady, Zavier; Marre, Olivier; Bartolozzi, Chiara; Panzeri, Stefano; Benosman, Ryad

    2015-03-01

    This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray levels into spike timings is currently at the basis of almost every spike-based modeling of biological visual systems. The use of images naturally leads to generating incorrect artificial and redundant spike timings and, more important, also contradicts biological findings indicating that visual processing is massively parallel, asynchronous with high temporal resolution. A new concept for acquiring visual information through pixel-individual asynchronous level-crossing sampling has been proposed in a recent generation of asynchronous neuromorphic visual sensors. Unlike conventional cameras, these sensors acquire data not at fixed points in time for the entire array but at fixed amplitude changes of their input, resulting optimally sparse in space and time-pixel individually and precisely timed only if new, (previously unknown) information is available (event based). This letter uses the high temporal resolution spiking output of neuromorphic event-based visual sensors to show that lowering time precision degrades performance on several recognition tasks specifically when reaching the conventional range of machine vision acquisition frequencies (30-60 Hz). The use of information theory to characterize separability between classes for each temporal resolution shows that high temporal acquisition provides up to 70% more information that conventional spikes generated from frame-based acquisition as used in standard artificial vision, thus drastically increasing the separability between classes of objects. Experiments on real data show that the amount of information loss is correlated with temporal precision. Our information-theoretic study highlights the potentials of neuromorphic asynchronous visual sensors for both practical applications and theoretical

  11. Spiking patterns of a hippocampus model in electric fields

    Institute of Scientific and Technical Information of China (English)

    Men Cong; Wang Jiang; Qin Ying-Mei; Wei Xi-Le; Che Yan-Qiu; Deng Bin

    2011-01-01

    We develop a model of CA3 neurons embedded in a resistive array to mimic the effects of electric fields from a new perspective.Effects of DC and sinusoidal electric fields on firing patterns in CA3 neurons are investigated in this study.The firing patterns can be switched from no firing pattern to burst or from burst to fast periodic firing pattern with the increase of DC electric field intensity.It is also found that the firing activities are sensitive to the frequency and amplitude of the sinusoidal electric field.Different phase-locking states and chaotic firing regions are observed in the parameter space of frequency and amplitude.These findings are qualitatively in accordance with the results of relevant experimental and numerical studies.It is implied that the external or endogenous electric field can modulate the neural code in the brain.Furthermore,it is helpful to develop control strategies based on electric fields to control neural diseases such as epilepsy.

  12. Numerical simulation of neuronal spike patterns in a retinal network model

    Institute of Scientific and Technical Information of China (English)

    Lei Wang; Shenquan Liu; Shanxing Ou

    2011-01-01

    This study utilized a neuronal compartment model and NEURON software to study the effects of external light stimulation on retinal photoreceptors and spike patterns of neurons in a retinal network. Following light stimulation of different shapes and sizes, changes in the spike features of ganglion cells indicated that different shapes of light stimulation elicited different retinal responses. By manipulating the shape of light stimulation, we investigated the effects of the large number of electrical synapses existing between retinal neurons. Model simulation and analysis suggested that interplexiform cells play an important role in visual signal information processing in the retina, and the findings indicated that our constructed retinal network model was reliable and feasible. In addition, the simulation results demonstrated that ganglion cells exhibited a variety of spike patterns under different light stimulation sizes and different stimulation shapes, which reflect the functions of the retina in signal transmission and processing.

  13. Scan patterns when viewing natural scenes: Emotion, complexity, and repetition

    OpenAIRE

    Bradley, Margaret M.; Houbova, Petra; Miccoli,Laura; Costa, Vincent D.; Lang, Peter J.

    2011-01-01

    Eye movements were monitored during picture viewing and effects of hedonic content, perceptual composition, and repetition on scanning assessed. In Experiment 1, emotional and neutral pictures that were figure-ground compositions or more complex scenes were presented for a 6 s free viewing period. Viewing emotional pictures or complex scenes prompted more fixations and broader scanning of the visual array, compared to neutral pictures or simple figure-ground compositions. Effects of emotion a...

  14. Concept mediation in trilingual translation: evidence from response time and repetition priming patterns.

    Science.gov (United States)

    Francis, Wendy S; Gallard, Sabrina L K

    2005-12-01

    Translation responses to individual words were elicited from 48 English-Spanish-French trilinguals, who translated in six directions at study and two directions at test. Patterns of translation response times and error rates at study reflected the relative proficiency of the trilinguals in comprehension and production of their three languages. At test, repeated items were translated more quickly than new items, with the strongest priming effects occurring for identical repetitions. Repetition priming was also substantial when only the stimulus language or only the response language matched from study to test, implying that repeated comprehension and production processes contribute to priming in translation. Patterns of response times and repetition priming indicate that translation in all directions involved conceptual access. Additive patterns in response time asymmetries and repetition priming were consistent with the treatment of word comprehension and production processes of translation as independent.

  15. Repetition rate tunable ultra-short optical pulse generation based on electrical pattern generator

    Institute of Scientific and Technical Information of China (English)

    Xin Fu; Hongming Zhang; Meng Yan; Minyu Yao

    2009-01-01

    @@ An actively mode-locked laser with tunable repetition rate is proposed and experimentally demonstrated based on a programmable electrical pattern generator.By changing the repetition rate of the electrical patterns applied on the in-cavity modulator, the repetition rate of the output optical pulse sequences changes accordingly while the pulse width of the optical pulse train remains almost constant.In other words, the output ultra-short pulse train has a tunable duty cycle.In a proof-of-principle experiment, optical pulses with repetition rates of 10, 5, 2.5 and 1.25 GHz are obtained by adjusting the electrical pattern applied on the in-cavity modulator while their pulse widths remain almost unchanged.

  16. Ca2+-activated K+ (BK) channel inactivation contributes to spike broadening during repetitive firing in the rat lateral amygdala.

    Science.gov (United States)

    Faber, E S Louise; Sah, Pankaj

    2003-10-15

    In many neurons, trains of action potentials show frequency-dependent broadening. This broadening results from the voltage-dependent inactivation of K+ currents that contribute to action potential repolarisation. In different neuronal cell types these K+ currents have been shown to be either slowly inactivating delayed rectifier type currents or rapidly inactivating A-type voltage-gated K+ currents. Recent findings show that inactivation of a Ca2+-dependent K+ current, mediated by large conductance BK-type channels, also contributes to spike broadening. Here, using whole-cell recordings in acute slices, we examine spike broadening in lateral amygdala projection neurons. Spike broadening is frequency dependent and is reversed by brief hyperpolarisations. This broadening is reduced by blockade of voltage-gated Ca2+ channels and BK channels. In contrast, broadening is not blocked by high concentrations of 4-aminopyridine (4-AP) or alpha-dendrotoxin. We conclude that while inactivation of BK-type Ca2+-activated K+ channels contributes to spike broadening in lateral amygdala neurons, inactivation of another as yet unidentified outward current also plays a role.

  17. Decoding of retinal ganglion cell spike trains evoked by temporally patterned electrical stimulation.

    Science.gov (United States)

    Ryu, Sang Baek; Ye, Jang Hee; Goo, Yong Sook; Kim, Chi Hyun; Kim, Kyung Hwan

    2010-08-12

    For successful restoration of vision by retinal prostheses, the neural activity of retinal ganglion cells (RGCs) evoked by electrical stimulation should represent the information of spatiotemporal patterns of visual input. We propose a method to evaluate the effectiveness of stimulation pulse trains so that the crucial temporal information of a visual input is accurately represented in the RGC responses as the amplitudes of pulse trains are modulated according to the light intensity. This was enabled by spike train decoding. The effectiveness of the stimulation was evaluated by the accuracy of decoding pulse amplitude from the RGC spike train, i.e., by the similarity between the original and the decoded pulse amplitude time series. When the parameters of stimulation were suitably determined, the RGC responses were reliably modulated by varying the amplitude of electrical pulses. Accordingly, the temporal pattern of pulse amplitudes could be successfully decoded from multiunit RGC spike trains. The range of pulse amplitude and the pulse rate were critical for accurate representation of input information in RGC responses. These results suggest that pulse amplitude modulation is a feasible means to encode temporal visual information by RGC spike trains and thus to implement stimulus encoding strategies for retinal prostheses.

  18. Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity.

    Directory of Open Access Journals (Sweden)

    Riccardo Storchi

    Full Text Available BACKGROUND: Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns. METHODOLOGY/PRINCIPAL FINDINGS: Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences, whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R to evaluate the average pattern complexity, the structure of essential classes and their stability in time. CONCLUSIONS/SIGNIFICANCE: We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of

  19. Discovering Patterns in Multi-neuronal Spike Trains using the Frequent Episode Method

    CERN Document Server

    Unnikrishnan, K P; Sastry, P S

    2007-01-01

    Discovering the 'Neural Code' from multi-neuronal spike trains is an important task in neuroscience. For such an analysis, it is important to unearth interesting regularities in the spiking patterns. In this report, we present an efficient method for automatically discovering synchrony, synfire chains, and more general sequences of neuronal firings. We use the Frequent Episode Discovery framework of Laxman, Sastry, and Unnikrishnan (2005), in which the episodes are represented and recognized using finite-state automata. Many aspects of functional connectivity between neuronal populations can be inferred from the episodes. We demonstrate these using simulated multi-neuronal data from a Poisson model. We also present a method to assess the statistical significance of the discovered episodes. Since the Temporal Data Mining (TDM) methods used in this report can analyze data from hundreds and potentially thousands of neurons, we argue that this framework is appropriate for discovering the `Neural Code'.

  20. Coordination in Fast Repetitive Violin-Bowing Patterns

    Science.gov (United States)

    Schoonderwaldt, Erwin; Altenmüller, Eckart

    2014-01-01

    We present a study of coordination behavior in complex violin-bowing patterns involving simultaneous bow changes (reversal of bowing direction) and string crossings (changing from one string to another). Twenty-two violinists (8 advanced amateurs, 8 students with violin as major subject, and 6 elite professionals) participated in the experiment. We investigated the influence of a variety of performance conditions (specific bowing patterns, dynamic level, tempo, and transposition) and level of expertise on coordination behavior (a.o., relative phase and amplitude) and stability. It was found that the general coordination behavior was highly consistent, characterized by a systematic phase lead of bow inclination over bow velocity of about 15° (i.e., string crossings were consistently timed earlier than bow changes). Within similar conditions, a high individual consistency was found, whereas the inter-individual agreement was considerably less. Furthermore, systematic influences of performance conditions on coordination behavior and stability were found, which could be partly explained in terms of particular performance constraints. Concerning level of expertise, only subtle differences were found, the student and professional groups (higher level of expertise) showing a slightly higher stability than the amateur group (lower level of expertise). The general coordination behavior as observed in the current study showed a high agreement with perceptual preferences reported in an earlier study to similar bowing patterns, implying that complex bowing trajectories for an important part emerge from auditory-motor interaction. PMID:25207542

  1. Sources of Phoneme Errors in Repetition: Perseverative, Neologistic, and Lesion Patterns in Jargon Aphasia

    Directory of Open Access Journals (Sweden)

    Emma Pilkington

    2017-05-01

    Full Text Available This study examined patterns of neologistic and perseverative errors during word repetition in fluent Jargon aphasia. The principal hypotheses accounting for Jargon production indicate that poor activation of a target stimulus leads to weakly activated target phoneme segments, which are outcompeted at the phonological encoding level. Voxel-lesion symptom mapping studies of word repetition errors suggest a breakdown in the translation from auditory-phonological analysis to motor activation. Behavioral analyses of repetition data were used to analyse the target relatedness (Phonological Overlap Index: POI of neologistic errors and patterns of perseveration in 25 individuals with Jargon aphasia. Lesion-symptom analyses explored the relationship between neurological damage and jargon repetition in a group of 38 aphasia participants. Behavioral results showed that neologisms produced by 23 jargon individuals contained greater degrees of target lexico-phonological information than predicted by chance and that neologistic and perseverative production were closely associated. A significant relationship between jargon production and lesions to temporoparietal regions was identified. Region of interest regression analyses suggested that damage to the posterior superior temporal gyrus and superior temporal sulcus in combination was best predictive of a Jargon aphasia profile. Taken together, these results suggest that poor phonological encoding, secondary to impairment in sensory-motor integration, alongside impairments in self-monitoring result in jargon repetition. Insights for clinical management and future directions are discussed.

  2. Calcium spikes, waves and oscillations in a large, patterned epithelial tissue.

    Science.gov (United States)

    Balaji, Ramya; Bielmeier, Christina; Harz, Hartmann; Bates, Jack; Stadler, Cornelia; Hildebrand, Alexander; Classen, Anne-Kathrin

    2017-02-20

    While calcium signaling in excitable cells, such as muscle or neurons, is extensively characterized, calcium signaling in epithelial tissues is little understood. Specifically, the range of intercellular calcium signaling patterns elicited by tightly coupled epithelial cells and their function in the regulation of epithelial characteristics are little explored. We found that in Drosophila imaginal discs, a widely studied epithelial model organ, complex spatiotemporal calcium dynamics occur. We describe patterns that include intercellular waves traversing large tissue domains in striking oscillatory patterns as well as spikes confined to local domains of neighboring cells. The spatiotemporal characteristics of intercellular waves and oscillations arise as emergent properties of calcium mobilization within a sheet of gap-junction coupled cells and are influenced by cell size and environmental history. While the in vivo function of spikes, waves and oscillations requires further characterization, our genetic experiments suggest that core calcium signaling components guide actomyosin organization. Our study thus suggests a possible role for calcium signaling in epithelia but importantly, introduces a model epithelium enabling the dissection of cellular mechanisms supporting the initiation, transmission and regeneration of long-range intercellular calcium waves and the emergence of oscillations in a highly coupled multicellular sheet.

  3. Calcium spikes, waves and oscillations in a large, patterned epithelial tissue

    Science.gov (United States)

    Balaji, Ramya; Bielmeier, Christina; Harz, Hartmann; Bates, Jack; Stadler, Cornelia; Hildebrand, Alexander; Classen, Anne-Kathrin

    2017-01-01

    While calcium signaling in excitable cells, such as muscle or neurons, is extensively characterized, calcium signaling in epithelial tissues is little understood. Specifically, the range of intercellular calcium signaling patterns elicited by tightly coupled epithelial cells and their function in the regulation of epithelial characteristics are little explored. We found that in Drosophila imaginal discs, a widely studied epithelial model organ, complex spatiotemporal calcium dynamics occur. We describe patterns that include intercellular waves traversing large tissue domains in striking oscillatory patterns as well as spikes confined to local domains of neighboring cells. The spatiotemporal characteristics of intercellular waves and oscillations arise as emergent properties of calcium mobilization within a sheet of gap-junction coupled cells and are influenced by cell size and environmental history. While the in vivo function of spikes, waves and oscillations requires further characterization, our genetic experiments suggest that core calcium signaling components guide actomyosin organization. Our study thus suggests a possible role for calcium signaling in epithelia but importantly, introduces a model epithelium enabling the dissection of cellular mechanisms supporting the initiation, transmission and regeneration of long-range intercellular calcium waves and the emergence of oscillations in a highly coupled multicellular sheet. PMID:28218282

  4. Multineuronal Spike Sequences Repeat with Millisecond Precision

    Directory of Open Access Journals (Sweden)

    Koki eMatsumoto

    2013-06-01

    Full Text Available Cortical microcircuits are nonrandomly wired by neurons. As a natural consequence, spikes emitted by microcircuits are also nonrandomly patterned in time and space. One of the prominent spike organizations is a repetition of fixed patterns of spike series across multiple neurons. However, several questions remain unsolved, including how precisely spike sequences repeat, how the sequences are spatially organized, how many neurons participate in sequences, and how different sequences are functionally linked. To address these questions, we monitored spontaneous spikes of hippocampal CA3 neurons ex vivo using a high-speed functional multineuron calcium imaging technique that allowed us to monitor spikes with millisecond resolution and to record the location of spiking and nonspiking neurons. Multineuronal spike sequences were overrepresented in spontaneous activity compared to the statistical chance level. Approximately 75% of neurons participated in at least one sequence during our observation period. The participants were sparsely dispersed and did not show specific spatial organization. The number of sequences relative to the chance level decreased when larger time frames were used to detect sequences. Thus, sequences were precise at the millisecond level. Sequences often shared common spikes with other sequences; parts of sequences were subsequently relayed by following sequences, generating complex chains of multiple sequences.

  5. Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution.

    Science.gov (United States)

    Espinal, Andres; Rostro-Gonzalez, Horacio; Carpio, Martin; Guerra-Hernandez, Erick I; Ornelas-Rodriguez, Manuel; Sotelo-Figueroa, Marco

    2016-01-01

    This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented.

  6. Variability of spike trains and the processing of temporal patterns of acoustic signals-problems, constraints, and solutions.

    Science.gov (United States)

    Ronacher, B; Franz, A; Wohlgemuth, S; Hennig, R M

    2004-04-01

    Object recognition and classification by sensory pathways is rooted in spike trains provided by sensory neurons. Nervous systems had to evolve mechanisms to extract information about relevant object properties, and to separate these from spurious features. In this review, problems caused by spike train variability and counterstrategies are exemplified for the processing of acoustic signals in orthopteran insects. Due to size limitations of their nervous system we expect to find solutions that are stripped to the computational basics. A key feature of auditory systems is temporal resolution, which is likely limited by spike train variability. Basic strategies to reduce such variability are to integrate over time, or to average across several neurons. The first strategy is constrained by its possible interference with temporal resolution. Grasshoppers do not seem to explore temporal integration much, in spite of the repetitive structure of their songs, which invites for 'multiple looks' at the signal. The benefits of averaging across neurons depend on uncorrelated responses, a factor that may be crucial for the performance and evolution of small nervous systems. In spite of spike train variability the temporal information necessary for the recognition of conspecifics is preserved to a remarkable degree in the auditory pathway.

  7. Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution

    Directory of Open Access Journals (Sweden)

    Andrés Espinal

    2016-07-01

    Full Text Available This paper deals with the design of Spiking Central Pattern Generators (SCPGs to achieve locomotion at different frequencies in legged robots and their hardware implementation in a FPGA and validation on a real hexapod robot. Herein, the SCPGs are automatically designed by a Christiansen Grammar Evolution (CGE-based methodology. It is, the CGE performs a solution for the configurations (synaptic weights and connections of each neuron in the SCPG. This is carried out through the indirect representation of a candidate solution that evolves to replicate a specific spike train according to a locomotion pattern (gait by measuring the similarity between the spike train with the SPIKE-Distance to drive the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the fitness function to achieve the SPIKE-distance criteria, such as: looking for SNNs with minimal connectivity or a CPG able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 DOFs hexapod robot is presented.

  8. Spiking patterns and their functional implications in the antennal lobe of the tobacco hornworm Manduca sexta.

    Directory of Open Access Journals (Sweden)

    Hong Lei

    Full Text Available Bursting as well as tonic firing patterns have been described in various sensory systems. In the olfactory system, spontaneous bursts have been observed in neurons distributed across several synaptic levels, from the periphery, to the olfactory bulb (OB and to the olfactory cortex. Several in vitro studies indicate that spontaneous firing patterns may be viewed as "fingerprints" of different types of neurons that exhibit distinct functions in the OB. It is still not known, however, if and how neuronal burstiness is correlated with the coding of natural olfactory stimuli. We thus conducted an in vivo study to probe this question in the OB equivalent structure of insects, the antennal lobe (AL of the tobacco hornworm Manduca sexta. We found that in the moth's AL, both projection (output neurons (PNs and local interneurons (LNs are spontaneously active, but PNs tend to produce spike bursts while LNs fire more regularly. In addition, we found that the burstiness of PNs is correlated with the strength of their responses to odor stimulation--the more bursting the stronger their responses to odors. Moreover, the burstiness of PNs was also positively correlated with the spontaneous firing rate of these neurons, and pharmacological reduction of bursting resulted in a decrease of the neurons' responsiveness. These results suggest that neuronal burstiness reflects a physiological state of these neurons that is directly linked to their response characteristics.

  9. Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

    Science.gov (United States)

    Bennett, James E. M.; Bair, Wyeth

    2015-01-01

    Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

  10. Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns.

    Science.gov (United States)

    Kauppi, Jukka-Pekka; Martikainen, Kalle; Ruotsalainen, Ulla

    2010-12-01

    The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns. Copyright © 2010 Elsevier Ltd. All rights reserved.

  11. Pattern separation and synchronization in spiking associative memories and visual areas.

    Science.gov (United States)

    Knoblauch, A; Palm, G

    2001-01-01

    Scene analysis in the mammalian visual system, conceived as a distributed and parallel process, faces the so-called binding problem. As a possible solution, the temporal correlation hypothesis has been suggested and implemented in phase-coding models. We propose an alternative model that reproduces experimental findings of synchronized and desynchronized fast oscillations more closely. This model is based on technical considerations concerning improved pattern separation in associative memories on the one hand, and on known properties of the visual cortex on the other. It consists of two reciprocally connected areas, one corresponding to a peripheral visual area (P), the other a central association area (C). P implements the orientation-selective subsystem of the primary visual cortex, while C was modeled as an associative memory with connections formed by Hebbian learning of all assemblies corresponding to stimulus objects. Spiking neurons including habituation and correlated noise were incorporated as well as realistic synaptic delays. Three learned stimuli were presented simultaneously and correlation analysis was performed on spike recordings. Generally, we found two states of activity: (i) relatively slow and unordered oscillations at about 20-25 Hz, synchronized only within small regions; and (ii) faster and more precise oscillations around 50-60 Hz, synchronized over the whole simulated area. The neuron groups representing one stimulus tended to be simultaneously in either the slow or the fast state. At each particular time, only one assembly was found to be in the fast state. Activation of the three assemblies switched on a time scale of 100 ms. This can be interpreted as self-generated attention switching. On the time scale corresponding to gamma oscillations, cross correlations between local neuron groups were either modulated or flat. Modulated correlograms resulted if the groups coded features corresponding to a common object. Otherwise, the

  12. Classification of correlated patterns with a configurable analog VLSI neural network of spiking neurons and self-regulating plastic synapses.

    Science.gov (United States)

    Giulioni, Massimilian; Pannunzi, Mario; Badoni, Davide; Dante, Vittorio; Del Giudice, Paolo

    2009-11-01

    We describe the implementation and illustrate the learning performance of an analog VLSI network of 32 integrate-and-fire neurons with spike-frequency adaptation and 2016 Hebbian bistable spike-driven stochastic synapses, endowed with a self-regulating plasticity mechanism, which avoids unnecessary synaptic changes. The synaptic matrix can be flexibly configured and provides both recurrent and external connectivity with address-event representation compliant devices. We demonstrate a marked improvement in the efficiency of the network in classifying correlated patterns, owing to the self-regulating mechanism.

  13. Repetitive Reduction Patterns in Lambda Calculus with letrec (Work in Progress

    Directory of Open Access Journals (Sweden)

    Jan Rochel

    2011-02-01

    Full Text Available For the lambda-calculus with letrec we develop an optimisation, which is based on the contraction of a certain class of 'future' (also: virtual redexes. In the implementation of functional programming languages it is common practice to perform beta-reductions at compile time whenever possible in order to produce code that requires fewer reductions at run time. This is, however, in principle limited to redexes and created redexes that are 'visible' (in the sense that they can be contracted without the need for unsharing, and cannot generally be extended to redexes that are concealed by sharing constructs such as letrec. In the case of recursion, concealed redexes become visible only after unwindings during evaluation, and then have to be contracted time and again. We observe that in some cases such redexes exhibit a certain form of repetitive behaviour at run time. We describe an analysis for identifying binders that give rise to such repetitive reduction patterns, and eliminate them by a sort of predictive contraction. Thereby these binders are lifted out of recursive positions or eliminated altogether, as a result alleviating the amount of beta-reductions required for each recursive iteration. Both our analysis and simplification are suitable to be integrated into existing compilers for functional programming languages as an additional optimisation phase. With this work we hope to contribute to increasing the efficiency of executing programs written in such languages.

  14. Cortical Sensitivity to Guitar Note Patterns: EEG Entrainment to Repetition and Key

    Science.gov (United States)

    Bridwell, David A.; Leslie, Emily; McCoy, Dakarai Q.; Plis, Sergey M.; Calhoun, Vince D.

    2017-01-01

    Music is ubiquitous throughout recent human culture, and many individual's have an innate ability to appreciate and understand music. Our appreciation of music likely emerges from the brain's ability to process a series of repeated complex acoustic patterns. In order to understand these processes further, cortical responses were measured to a series of guitar notes presented with a musical pattern or without a pattern. ERP responses to individual notes were measured using a 24 electrode Bluetooth mobile EEG system (Smarting mBrainTrain) while 13 healthy non-musicians listened to structured (i.e., within musical keys and with repetition) or random sequences of guitar notes for 10 min each. We demonstrate an increased amplitude to the ERP that appears ~200 ms to notes presented within the musical sequence. This amplitude difference between random notes and patterned notes likely reflects individual's cortical sensitivity to guitar note patterns. These amplitudes were compared to ERP responses to a rare note embedded within a stream of frequent notes to determine whether the sensitivity to complex musical structure overlaps with the sensitivity to simple irregularities reflected in traditional auditory oddball experiments. Response amplitudes to the negative peak at ~175 ms are statistically correlated with the mismatch negativity (MMN) response measured to a rare note presented among a series of frequent notes (i.e., in a traditional oddball sequence), but responses to the subsequent positive peak at ~200 do not show a statistical relationship with the P300 response. Thus, the sensitivity to musical structure identified to 4 Hz note patterns appears somewhat distinct from the sensitivity to statistical regularities reflected in the traditional “auditory oddball” sequence. Overall, we suggest that this is a promising approach to examine individual's sensitivity to complex acoustic patterns, which may overlap with higher level cognitive processes, including

  15. Neural network based pattern matching and spike detection tools and services--in the CARMEN neuroinformatics project.

    Science.gov (United States)

    Fletcher, Martyn; Liang, Bojian; Smith, Leslie; Knowles, Alastair; Jackson, Tom; Jessop, Mark; Austin, Jim

    2008-10-01

    In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets.

  16. Monitoring spike train synchrony

    CERN Document Server

    Kreuz, Thomas; Houghton, Conor; Andrzejak, Ralph G; Mormann, Florian

    2012-01-01

    Recently, the SPIKE-distance has been proposed as a parameter-free and time-scale independent measure of spike train synchrony. This measure is time-resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for event-like firing patterns. Here we present a substantial improvement of this measure which eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real-time. We demonstrate that these methods are capable of extracting valuable information from ...

  17. Spikes and bursts in two types of thalamic projection neurons differentially shape sleep patterns and auditory responses in a songbird.

    Science.gov (United States)

    Hahnloser, Richard H R; Wang, Claude Z-H; Nager, Aymeric; Naie, Katja

    2008-05-07

    In mammals, the thalamus plays important roles for cortical processing, such as relay of sensory information and induction of rhythmical firing during sleep. In neurons of the avian cerebrum, in analogy with cortical up and down states, complex patterns of regular-spiking and dense-bursting modes are frequently observed during sleep. However, the roles of thalamic inputs for shaping these firing modes are largely unknown. A suspected key player is the avian thalamic nucleus uvaeformis (Uva). Uva is innervated by polysensory input, receives indirect cerebral feedback via the midbrain, and projects to the cerebrum via two distinct pathways. Using pharmacological manipulation, electrical stimulation, and extracellular recordings of Uva projection neurons, we study the involvement of Uva in zebra finches for the generation of spontaneous activity and auditory responses in premotor area HVC (used as a proper name) and the downstream robust nucleus of the arcopallium (RA). In awake and sleeping birds, we find that single Uva spikes suppress and spike bursts enhance spontaneous and auditory-evoked bursts in HVC and RA neurons. Strong burst suppression is mediated mainly via tonically firing HVC-projecting Uva neurons, whereas a fast burst drive is mediated indirectly via Uva neurons projecting to the nucleus interface of the nidopallium. Our results reveal that cerebral sleep-burst epochs and arousal-related burst suppression are both shaped by sophisticated polysynaptic thalamic mechanisms.

  18. Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks

    DEFF Research Database (Denmark)

    Garrido, Jesús A.; Luque, Niceto R.; Tolu, Silvia

    2016-01-01

    understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input synapses...... and at the inhibitory interneuron-interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity...... patterns. The combination of plasticity in lateral inhibitory connections and homeostatic mechanisms in the inhibitory interneurons optimized mutual information (MI) transfer. The storage of multiple complex patterns in plastic interneuron networks could be critical for the generation of sparse...

  19. [Coordination patterns assessed by a continuous measure of joints coupling during upper limb repetitive movements].

    Science.gov (United States)

    Draicchio, F; Silvetti, A; Ranavolo, A; Iavicoli, S

    2008-01-01

    We analyzed the coordination patterns between elbow, shoulder and trunk in a motor task consisting of reaching out, picking up a cylinder, and transporting it back by using the Dynamical Systems Theory and calculating the continuous relative phase (CRP), a continuous measure of the coupling between two interacting joints. We used an optoelectronic motion analysis system consisting of eight infra-red ray cameras to detect the movements of nine skin-mounted markers. We calculated the root square of the adjusted coefficient of determination, the coefficient of multiple correlation (CMC), in order to investigate the repeatability of the joints coordination. The data confirm that the CNS establishes both synergic (i.e. coupling between shoulder and trunk on the frontal plane) and hierarchical (i.e. coupling between elbow-shoulder-trunk on the horizontal plane) relationships among the available degrees of freedom to overcome the complexity due to motor redundancy. The present study describes a method to investigate the organization of the kinematic degrees of freedom during upper limb multi-joint motor tasks that can be useful to assess upper limb repetitive movements.

  20. Repetitive Patterns in Rapid Optical Variations in the Nearby Black-hole Binary V404 Cygni

    CERN Document Server

    Kimura, Mariko; Kato, Taichi; Ueda, Yoshihiro; Nakahira, Satoshi; Shidatsu, Megumi; Enoto, Teruaki; Hori, Takafumi; Nogami, Daisaku; Littlefield, Colin; Ishioka, Ryoko; Chen, Ying-Tung; King, Sun-Kun; Wen, Chih-Yi; Wang, Shiang-Yu; Lehner, Matthew J; Schwamb, Megan E; Wang, Jen-Hung; Zhang, Zhi-Wei; Alcock, Charles; Axelrod, Tim; Bianco, Federica B; Byun, Yong-Ik; Chen, Wen-Ping; Cook, Kem H; Kim, Dae-Won; Lee, Typhoon; Marshall, Stuart L; Pavlenko, Elena P; Antonyuk, Oksana I; Antonyuk, Kirill A; Pit, Nikolai V; Sosnovskij, Aleksei A; Babina, Julia V; Baklanov, Aleksei V; Pozanenko, Alexei S; Mazaeva, Elena D; Schmalz, Sergei E; Reva, Inna V; Belan, Sergei P; Inasaridze, Raguli Ya; Tungalag, Namkhai; Volnova, Alina A; Molotov, Igor E; de Miguel, Enrique; Kasai, Kiyoshi; Stein, William; Dubovsky, Pavol A; Kiyota, Seiichiro; Miller, Ian; Richmond, Michael; Goff, William; Andreev, Maksim V; Takahashi, Hiromitsu; Kojiguchi, Naoto; Sugiura, Yuki; Takeda, Nao; Yamada, Eiji; Matsumoto, Katsura; James, Nick; Pickard, Roger D; Tordai, Tamás; Maeda, Yutaka; Ruiz, Javier; Miyashita, Atsushi; Cook, Lewis M; Imada, Akira; Uemura, Makoto

    2016-01-01

    How black holes accrete surrounding matter is a fundamental, yet unsolved question in astrophysics. It is generally believed that matter is absorbed into black holes via accretion disks, the state of which depends primarily on the mass-accretion rate. When this rate approaches the critical rate (the Eddington limit), thermal instability is supposed to occur in the inner disc, causing repetitive patterns of large-amplitude X-ray variability (oscillations) on timescales of minutes to hours. In fact, such oscillations have been observed only in sources with a high mass accretion rate, such as GRS 1915+105. These large-amplitude, relatively slow timescale, phenomena are thought to have physical origins distinct from X-ray or optical variations with small amplitudes and fast ($\\lesssim$10 sec) timescales often observed in other black hole binaries (e.g., XTE J1118+480 and GX 339-4). Here we report an extensive multi-colour optical photometric data set of V404 Cygni, an X-ray transient source containing a black hol...

  1. Striatal fast-spiking interneurons: from firing patterns to postsynaptic impact

    Directory of Open Access Journals (Sweden)

    Andreas eKlaus

    2011-07-01

    Full Text Available In the striatal microcircuit, fast-spiking (FS interneurons have an important role in mediating inhibition onto neighboring medium spiny (MS projection neurons. In this study, we combined computational modeling with in vitro and in vivo electrophysiological measurements to investigate FS cells in terms of their discharge properties and their synaptic efficacies onto MS neurons. In vivo firing of striatal FS interneurons is characterized by a high firing variability. It is not known, however, if this variability results from the input that FS cells receive, or if it is promoted by the stuttering spike behavior of these neurons. Both our model and measurements in vitro show that FS neurons that exhibit random stuttering discharge in response to steady depolarization, do not show the typical stuttering behavior when they receive fluctuating input. Importantly, our model predicts that electrically coupled FS cells show substantial spike synchronization only when they are in the stuttering regime. Therefore, together with the lack of synchronized firing of striatal FS interneurons that has been reported in vivo, these results suggest that neighboring FS neurons are not in the stuttering regime simultaneously and that in vivo FS firing variability is more likely determined by the input fluctuations. Furthermore, the variability in FS firing is translated to variability in the postsynaptic amplitudes in MS neurons due to the strong synaptic depression of the FS-to-MS synapse. Our results support the idea that these synapses operate over a wide range from strongly depressed to almost fully recovered. The strong inhibitory effects that FS cells can impose on their postsynaptic targets, and the fact that the FS-to-MS synapse model showed substantial depression over extended periods of time might indicate the importance of cooperative effects of multiple presynaptic FS interneurons and the precise orchestration of their activity.

  2. Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks.

    Science.gov (United States)

    Garrido, Jesús A; Luque, Niceto R; Tolu, Silvia; D'Angelo, Egidio

    2016-08-01

    The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input synapses and at the inhibitory interneuron-interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input patterns. The combination of plasticity in lateral inhibitory connections and homeostatic mechanisms in the inhibitory interneurons optimized mutual information (MI) transfer. The storage of multiple complex patterns in plastic interneuron networks could be critical for the generation of sparse representations of information in excitatory neuron populations falling under their control.

  3. Repetitive patterns in rapid optical variations in the nearby black-hole binary V404 Cygni.

    Science.gov (United States)

    Kimura, Mariko; Isogai, Keisuke; Kato, Taichi; Ueda, Yoshihiro; Nakahira, Satoshi; Shidatsu, Megumi; Enoto, Teruaki; Hori, Takafumi; Nogami, Daisaku; Littlefield, Colin; Ishioka, Ryoko; Chen, Ying-Tung; King, Sun-Kun; Wen, Chih-Yi; Wang, Shiang-Yu; Lehner, Matthew J; Schwamb, Megan E; Wang, Jen-Hung; Zhang, Zhi-Wei; Alcock, Charles; Axelrod, Tim; Bianco, Federica B; Byun, Yong-Ik; Chen, Wen-Ping; Cook, Kem H; Kim, Dae-Won; Lee, Typhoon; Marshall, Stuart L; Pavlenko, Elena P; Antonyuk, Oksana I; Antonyuk, Kirill A; Pit, Nikolai V; Sosnovskij, Aleksei A; Babina, Julia V; Baklanov, Aleksei V; Pozanenko, Alexei S; Mazaeva, Elena D; Schmalz, Sergei E; Reva, Inna V; Belan, Sergei P; Inasaridze, Raguli Ya; Tungalag, Namkhai; Volnova, Alina A; Molotov, Igor E; de Miguel, Enrique; Kasai, Kiyoshi; Stein, William L; Dubovsky, Pavol A; Kiyota, Seiichiro; Miller, Ian; Richmond, Michael; Goff, William; Andreev, Maksim V; Takahashi, Hiromitsu; Kojiguchi, Naoto; Sugiura, Yuki; Takeda, Nao; Yamada, Eiji; Matsumoto, Katsura; James, Nick; Pickard, Roger D; Tordai, Tamás; Maeda, Yutaka; Ruiz, Javier; Miyashita, Atsushi; Cook, Lewis M; Imada, Akira; Uemura, Makoto

    2016-01-01

    How black holes accrete surrounding matter is a fundamental yet unsolved question in astrophysics. It is generally believed that matter is absorbed into black holes via accretion disks, the state of which depends primarily on the mass-accretion rate. When this rate approaches the critical rate (the Eddington limit), thermal instability is supposed to occur in the inner disk, causing repetitive patterns of large-amplitude X-ray variability (oscillations) on timescales of minutes to hours. In fact, such oscillations have been observed only in sources with a high mass-accretion rate, such as GRS 1915+105 (refs 2, 3). These large-amplitude, relatively slow timescale, phenomena are thought to have physical origins distinct from those of X-ray or optical variations with small amplitudes and fast timescales (less than about 10 seconds) often observed in other black-hole binaries-for example, XTE J1118+480 (ref. 4) and GX 339-4 (ref. 5). Here we report an extensive multi-colour optical photometric data set of V404 Cygni, an X-ray transient source containing a black hole of nine solar masses (and a companion star) at a distance of 2.4 kiloparsecs (ref. 8). Our data show that optical oscillations on timescales of 100 seconds to 2.5 hours can occur at mass-accretion rates more than ten times lower than previously thought. This suggests that the accretion rate is not the critical parameter for inducing inner-disk instabilities. Instead, we propose that a long orbital period is a key condition for these large-amplitude oscillations, because the outer part of the large disk in binaries with long orbital periods will have surface densities too low to maintain sustained mass accretion to the inner part of the disk. The lack of sustained accretion--not the actual rate--would then be the critical factor causing large-amplitude oscillations in long-period systems.

  4. Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

    Science.gov (United States)

    Woodward, Alexander; Froese, Tom; Ikegami, Takashi

    2015-02-01

    The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Response Features Determining Spike Times

    Directory of Open Access Journals (Sweden)

    Barry J. Richmond

    1999-01-01

    redundant with that carried by the coarse structure. Thus, the existence of precisely timed spike patterns carrying stimulus-related information does not imply control of spike timing at precise time scales.

  6. Coding for stimulus velocity by temporal patterning of spike discharges in visual cells of cat superior colliculus.

    Science.gov (United States)

    Mandl, G

    1993-07-01

    Statistical analyses, performed on extracellularly recorded spike trains generated by 69 single motion sensitive visual cells in the intermediate layers of superior colliculi of pretrigeminal cat preparations, revealed that--even in the unstimulated condition (38/69)--most neuronal spike discharge patterns tended to switch between two stochastically distinct states, in the form of rapidly alternating "bursting" (high frequency) and "resting" (low frequency) episodes. The numbers of consecutive interspike intervals within a given state were, as a rule, independent integer-valued random variables with discrete probability distributions, in essential agreement with the semi-Markov model proposed by Ekholm and Hyvärinen [(1970) Biophysical Journal, 10, 773-796]. The introduction of visual stimuli (47/69) moving with velocities of 2-160 deg/sec caused systematic and reproducible changes in the ratio of bursting to resting activities, decreases in overall discharge variability, and increases in signal transinformation flow. Moreover, with one group of stimulated cells (28/47), increasing stimulus velocity caused increasingly precise ("stimulus-forced") synchronization of bursting episodes with specific phases of stimulus movement; while for a smaller group (12/47), stimulus-related alternations between bursting and resting states assumed the form of semi-rhythmical burst discharges within the characteristic 60-80 Hz "gamma oscillation" range ("stimulus-induced" synchronization). For a minority of cells (7/47), switching between bursting and resting states--although characteristically modified by stimulus velocity--remained largely desynchronized with all phases of stimulus transit. It was argued that such temporal patterns of discharge may constitute elements of a candidate "distribution" code for movement detection by the cat visual system.

  7. Upper trapezius muscle activity patterns during repetitive manual material handling and work with with a computer mouse.

    Science.gov (United States)

    Jensen, C; Finsen, L; Hansen, K; Christensen, H

    1999-10-01

    Firstly, upper trapezius EMG activity patterns were recorded on the dominant side of 6 industrial production workers and on the side operating a computer mouse of 14 computer-aided design (CAD) operators to study differences in acute muscular response related to the repetitiveness of the exposure. The work tasks were performed with median arm movement frequencies ranging from 5 min(-1) to 13 min(-1) and were characterized by work cycle times ranging from less than 30 sec to several days. However, the static and median EMG levels and EMG gap frequencies were similar for all work tasks indicating that shoulder muscle loads may be unaffected by large variations in arm movement frequencies and work cycle times. An exposure variation analyses (EVA) showed that the EMG activity patterns recorded during production work were more repetitive than during CAD work, whereas CAD work was associated with more static muscle activity patterns, both may be associated with a risk of developing musculoskeletal symptoms. Secondly, upper trapezius EMG activity patterns recorded on the mouse side of the CAD operators were compared with those recorded on the non-mouse side to study differences in muscular responses potentially related to the risk of developing shoulder symptoms which were more prevalent on the mouse side. The number of EMG gaps on the mouse side were significantly lower than the values for the upper trapezius on the non-mouse side indicating that more continuous activity was present in the upper trapezius muscle on the mouse side and EVA analyses showed a more repetitive muscle activity pattern on the mouse side. These findings may be of importance to explain differences in the prevalence of shoulder symptoms.

  8. Monitoring spike train synchrony.

    Science.gov (United States)

    Kreuz, Thomas; Chicharro, Daniel; Houghton, Conor; Andrzejak, Ralph G; Mormann, Florian

    2013-03-01

    Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.

  9. Advance cueing produces enhanced action-boundary patterns of spike activity in the sensorimotor striatum

    OpenAIRE

    2011-01-01

    One of the most characteristic features of habitual behaviors is that they can be evoked by a single cue. In the experiments reported here, we tested for the effects of such advance cueing on the firing patterns of striatal neurons in the sensorimotor striatum. Rats ran in a T-maze with instruction cues about the location of reward given at the start of the runs. This advance cueing about reward produced a highly augmented task-bracketing pattern of activity at the beginning and end of proced...

  10. Advance cueing produces enhanced action-boundary patterns of spike activity in the sensorimotor striatum.

    Science.gov (United States)

    Barnes, Terra D; Mao, Jian-Bin; Hu, Dan; Kubota, Yasuo; Dreyer, Anna A; Stamoulis, Catherine; Brown, Emery N; Graybiel, Ann M

    2011-04-01

    One of the most characteristic features of habitual behaviors is that they can be evoked by a single cue. In the experiments reported here, we tested for the effects of such advance cueing on the firing patterns of striatal neurons in the sensorimotor striatum. Rats ran in a T-maze with instruction cues about the location of reward given at the start of the runs. This advance cueing about reward produced a highly augmented task-bracketing pattern of activity at the beginning and end of procedural task performance relative to the patterns found previously with midtask cueing. Remarkably, the largest increase in activity early during the T-maze runs was not associated with the instruction cues themselves, the earliest predictors of reward; instead, the highest peak of early activity was associated with the beginning of the motor period of the task. We suggest that the advance cueing, reducing midrun demands for decision making but adding a working-memory load, facilitated chunking of the maze runs as executable scripts anchored to sensorimotor aspects of the task and unencumbered by midtask decision-making demands. Our findings suggest that the acquisition of stronger task-bracketing patterns of striatal activity in the sensorimotor striatum could reflect this enhancement of behavioral chunking. Deficits in such representations of learned sequential behaviors could contribute to motor and cognitive problems in a range of neurological disorders affecting the basal ganglia, including Parkinson's disease.

  11. Stability of repetitive-sequence PCR patterns with respect to culture age and subculture frequency.

    Science.gov (United States)

    Kang, Hyunseok Peter; Dunne, W Michael

    2003-06-01

    To examine the stability of repetitive-sequence (rep) PCR profiles, six species of bacteria were subcultured to blood agar plates and DNA was extracted from the cultures after 24, 48, and 72 h of incubation at 35 degrees C. In addition, the same species were subcultured to fresh blood plates daily and DNA was extracted from the cultures after growth of 5, 10, and 15 subcultures, respectively. rep PCR analysis demonstrated that all rep PCR fingerprints from a single species were identical.

  12. Pattern self-repetition of fingerprints, lip prints, and palatal rugae among three generations of family: A forensic approach to identify family hierarchy.

    Science.gov (United States)

    Mala, Sankeerti; Rathod, Vanita; Pundir, Siddharth; Dixit, Sudhanshu

    2017-01-01

    The unique pattern and structural diversity of fingerprints, lip prints, palatal rugae, and their occurrence in different patterns among individuals make it questionable whether they are completely unique even in a family hierarchy? Do they have any repetition of the patterns among the generations? Or is this a mere chaos theory? The present study aims to assess the pattern self-repetition of fingerprints, lip prints, and palatal rugae among three generations of ten different families. The present study was conducted at Rungta College of Dental Science and Research, Bhilai, India. Participants birth by origin of Chhattisgarh were only included in the study. Thirty participants from three consecutive generations of ten different families were briefed about the purpose of the study, and their fingerprints, lip prints, and palatal rugae impression were recorded and analyzed for the pattern of self-repetition. Multiple comparisons among the generations and one-way analysis of variance test were performed using SPSS 20 trial version. Among the pattern of primary palatal rugae, 10% showed repetition in all the three generations. Thirty percent showed repetition of the pattern of thumb fingerprints in all the three generation. The pattern of lip prints in the middle 1/3(rd) of lower lip, 20% showed repetition in alternative generations. The evaluations of fingerprints, lip prints, and palatal rugae showed fractal dimensions, occurring variations in dimensions according to the complexity of each structure. Even though a minute self-repetition in the patterns of lip, thumb, and palate among the three consequent generations in a family was observed considering the sample size, these results need to be confirmed in a larger sample, either to establish the role of chaos theory in forensic science or identifying a particular pattern of the individual in his family hierarchy.

  13. Repetition priming of motor activity mediated by a central pattern generator: the importance of extrinsic vs. intrinsic program initiators.

    Science.gov (United States)

    Siniscalchi, Michael J; Cropper, Elizabeth C; Jing, Jian; Weiss, Klaudiusz R

    2016-10-01

    Repetition priming is characterized by increased performance as a behavior is repeated. Although this phenomenon is ubiquitous, mediating mechanisms are poorly understood. We address this issue in a model system, the feeding network of Aplysia This network generates both ingestive and egestive motor programs. Previous data suggest a chemical coding model: ingestive and egestive inputs to the feeding central pattern generator (CPG) release different modulators, which act via different second messengers to prime motor activity in different ways. The ingestive input to the CPG (neuron CBI-2) releases the peptides feeding circuit activating peptide and cerebral peptide 2, which produce an ingestive pattern of activity. The egestive input to the CPG (the esophageal nerve) releases the peptide small cardioactive peptide. This model is based on research that focused on a single aspect of motor control (radula opening). Here we ask whether repetition priming is observed if activity is triggered with a neuron within the core CPG itself and demonstrate that it is not. Moreover, previous studies demonstrated that effects of modulatory neurotransmitters that induce repetition priming persist. This suggests that it should be possible to "prime" motor programs triggered from within the CPG by first stimulating extrinsic modulatory inputs. We demonstrate that programs triggered after ingestive input activation are ingestive and programs triggered after egestive input activation are egestive. We ask where this priming occurs and demonstrate modifications within the CPG itself. This arrangement is likely to have important consequences for "task" switching, i.e., the cessation of one type of motor activity and the initiation of another. Copyright © 2016 the American Physiological Society.

  14. Wicket spikes: a case-control study of a benign eletroencephalografic variant pattern "Wicket spikes": estudo de variante eletrográfica benigna

    Directory of Open Access Journals (Sweden)

    MARCUS SABRY AZAR BATISTA

    1999-09-01

    Full Text Available Wicket spikes (WS are a benign eletroencephalogram (EEG variant, seen mainly in adults, during somnolence, in the temporal regions, in many clinical situations. WS can appear in trains or isolatedly, sometimes being difficult to differentiate from epileptiform activity. We reviewed 2,000 EEG's, found 65 with WS (3.25% and compared them with 65 normal EEG without WS. There was statistically significant (SS association between WS and age over 33; adolescent age was correlated to absence of WS and age over 65, to the presence of WS; there was an inverse correlation between WS and epilepsy, related to differences in age; a SS association with cerebrovascular disorders disappeared after controlling for age; a SS correlation with headache was also related to age; female predominance was not SS. There was a great variety of clinical situation associated with WS. We conclude that WS are a inespecific normal variant of the EEG that is age-related.As Wicket spikes (WS são um padrão benigno, variante da normalidade do eletrencefalograma (EEG, vistas principalmente em adultos, durante a sonolência, nas regiões temporais, em situações clínicas variadas. WS aparecem em "trens" ou isoladamente, podendo ser difícil diferenciá-las de atividade epileptiforme. Nós revisamos 2.000 EEG e encontramos 65 com WS (3,25% e os comparamos a 65 EEG 's normais sem WS. Encontramos associação estatisticamente significante (ES entre WS e idade acima de 33 anos; adolescência e ausência de WS e idade acima de 65 e presença de WS. Houve correlação inversa entre WS e epilepsia, explicada por diferenças nas médias de idade. A correlação ES entre WS e doença cerebrovascular desapareceu após controlarmos a idade. A correlação ES a cefaléia dependeu de sua relação à idade. A predominância do sexo feminino não foi ES. Houve maior variedade de situações clínicas associadas a WS. WS são uma variante normal do EEG, idade-relacionada.

  15. A Bioinformatics Approach for Detecting Repetitive Nested Motifs using Pattern Matching

    Science.gov (United States)

    Romero, José R.; Carballido, Jessica A.; Garbus, Ingrid; Echenique, Viviana C.; Ponzoni, Ignacio

    2016-01-01

    The identification of nested motifs in genomic sequences is a complex computational problem. The detection of these patterns is important to allow the discovery of transposable element (TE) insertions, incomplete reverse transcripts, deletions, and/or mutations. In this study, a de novo strategy for detecting patterns that represent nested motifs was designed based on exhaustive searches for pairs of motifs and combinatorial pattern analysis. These patterns can be grouped into three categories, motifs within other motifs, motifs flanked by other motifs, and motifs of large size. The methodology used in this study, applied to genomic sequences from the plant species Aegilops tauschii and Oryza sativa, revealed that it is possible to identify putative nested TEs by detecting these three types of patterns. The results were validated through BLAST alignments, which revealed the efficacy and usefulness of the new method, which is called Mamushka. PMID:27812277

  16. CHRONIC DIETARY EXPOSURE WITH INTERMITTENT SPIKE DOSES OF CHLORPYRIFOS FAILS TO ALTER FLASH OR PATTERN REVERSAL EVOKED POTENTIALS IN RATS.

    Science.gov (United States)

    Human exposure to pesticides is often characterized by chronic low level exposure with intermittent spiked higher exposures. Visual disturbances are often reported following exposure to xenobiotics, and cholinesterase-inhibiting compounds have been reported to alter visual functi...

  17. Persistence and storage of activity patterns in spiking recurrent cortical networks:Modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

    Directory of Open Access Journals (Sweden)

    Jesse ePalma

    2012-06-01

    Full Text Available Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM. Theorems in the 1970’s showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 milliseconds or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners

  18. Dynamical laser spike processing

    CERN Document Server

    Shastri, Bhavin J; Tait, Alexander N; Rodriguez, Alejandro W; Wu, Ben; Prucnal, Paul R

    2015-01-01

    Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved "spiking" of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate that graphene-coupled laser systems offer a unified low-level spike optical processing paradigm that goes well beyond previously studied laser dynamics. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation---fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system, but the addit...

  19. Leaders and followers: Quantifying consistency in spatio-temporal propagation patterns

    CERN Document Server

    Kreuz, Thomas; Pofahl, Martin; Mulansky, Mario

    2016-01-01

    Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a synfire pattern. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed SPIKE-Order and Spike Train Order, that define the Synfire Indicator value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neurosc...

  20. Efficacy of electrical stimulation of retinal ganglion cells with temporal patterns resembling light-evoked spike trains.

    Science.gov (United States)

    Wong, Raymond C S; Garrett, David J; Grayden, David B; Ibbotson, Michael R; Cloherty, Shaun L

    2014-01-01

    People with degenerative retinal diseases such as retinitis pigmentosa lose most of their photoreceptors but retain a significant proportion (~30%) of their retinal ganglion cells (RGCs). Microelectronic retinal prostheses aim to bypass the lost photoreceptors and restore vision by directly stimulating the surviving RGCs. Here we investigate the extent to which electrical stimulation of RGCs can evoke neural spike trains with statistics resembling those of normal visually-evoked responses. Whole-cell patch clamp recordings were made from individual cat RGCs in vitro. We first recorded the responses of each cell to short sequences of visual stimulation. These responses were converted to trains of electrical stimulation that we then presented to the same cell via an epiretinal stimulating electrode. We then quantified the efficacy of the electrical stimuli and the latency of the evoked spikes. In all cases, spikes were evoked with sub-millisecond latency (0.55 ms, median, ON cells, n = 8; 0.75 ms, median, OFF cells, n = 6) and efficacy ranged from 0.4-1.0 (0.79, median, ON cells; 0.97, median, OFF cells). These data demonstrate that meaningful spike trains, resembling normal responses of RGCs to visual stimulation, can be reliably evoked by epiretinal prostheses.

  1. Grid cell firing patterns may arise from feedback interaction between intrinsic rebound spiking and transverse travelling waves with multiple heading angles

    Directory of Open Access Journals (Sweden)

    Michael E Hasselmo

    2014-10-01

    Full Text Available This article presents a model using cellular resonance and rebound properties to model grid cells in medial entorhinal cortex. The model simulates the intrinsic resonance properties of single layer II stellate cells with different frequencies due to the hyperpolarization activated cation current (h current. The stellate cells generate rebound spikes after a delay interval that differs for neurons with different resonance frequency. Stellate cells drive inhibitory interneurons to cause rebound from inhibition in an alternating set of stellate cells that drive interneurons to activate the first set of cells. This allows maintenance of activity with cycle skipping of the spiking of cells that matches recent physiological data on theta cycle skipping. The rebound spiking interacts with subthreshold oscillatory input to stellate cells or interneurons regulated by medial septal input and defined relative to the spatial location coded by neurons. The timing of rebound determines whether the network maintains the activity for the same location or shifts to phases of activity representing a different location. Simulations show that spatial firing patterns similar to grid cells can be generated with a range of different resonance frequencies, indicating how grid cells could be generated with low frequencies present in bats and in mice with knockout of the HCN1 subunit of the h current.

  2. The Characteristics of Binary Spike-Time-Dependent Plasticity in HfO2-Based RRAM and Applications for Pattern Recognition

    Science.gov (United States)

    Zhou, Zheng; Liu, Chen; Shen, Wensheng; Dong, Zhen; Chen, Zhe; Huang, Peng; Liu, Lifeng; Liu, Xiaoyan; Kang, Jinfeng

    2017-04-01

    A binary spike-time-dependent plasticity (STDP) protocol based on one resistive-switching random access memory (RRAM) device was proposed and experimentally demonstrated in the fabricated RRAM array. Based on the STDP protocol, a novel unsupervised online pattern recognition system including RRAM synapses and CMOS neurons is developed. Our simulations show that the system can efficiently compete the handwritten digits recognition task, which indicates the feasibility of using the RRAM-based binary STDP protocol in neuromorphic computing systems to obtain good performance.

  3. Repetition and Translation Shifts

    Directory of Open Access Journals (Sweden)

    Simon Zupan

    2006-06-01

    Full Text Available Repetition manifests itself in different ways and at different levels of the text. The first basic type of repetition involves complete recurrences; in which a particular textual feature repeats in its entirety. The second type involves partial recurrences; in which the second repetition of the same textual feature includes certain modifications to the first occurrence. In the article; repetitive patterns in Edgar Allan Poe’s short story “The Fall of the House of Usher” and its Slovene translation; “Konec Usherjeve hiše”; are compared. The author examines different kinds of repetitive patterns. Repetitions are compared at both the micro- and macrostructural levels. As detailed analyses have shown; considerable microstructural translation shifts occur in certain types of repetitive patterns. Since these are not only occasional; sporadic phenomena; but are of a relatively high frequency; they reduce the translated text’s potential for achieving some of the gothic effects. The macrostructural textual property particularly affected by these shifts is the narrator’s experience as described by the narrative; which suffers a reduction in intensity.

  4. Repetitive maladaptive behavior: beyond repetition compulsion.

    Science.gov (United States)

    Bowins, Brad

    2010-09-01

    Maladaptive behavior that repeats, typically known as repetition compulsion, is one of the primary reasons that people seek psychotherapy. However, even with psychotherapeutic advances it continues to be extremely difficult to treat. Despite wishes and efforts to the contrary repetition compulsion does not actually achieve mastery, as evidenced by the problem rarely resolving without therapeutic intervention, and the difficulty involved in producing treatment gains. A new framework is proposed, whereby such behavior is divided into behavior of non-traumatic origin and traumatic origin with some overlap occurring. Repetitive maladaptive behavior of non-traumatic origin arises from an evolutionary-based process whereby patterns of behavior frequently displayed by caregivers and compatible with a child's temperament are acquired and repeated. It has a familiarity and ego-syntonic aspect that strongly motivates the person to retain the behavior. Repetitive maladaptive behavior of traumatic origin is characterized by defensive dissociation of the cognitive and emotional components of trauma, making it very difficult for the person to integrate the experience. The strong resistance of repetitive maladaptive behavior to change is based on the influence of both types on personality, and also factors specific to each. Psychotherapy, although very challenging at the best of times, can achieve the mastery wished and strived for, with the aid of several suggestions provided.

  5. HOW MANY REPETITIONS OF CHILD CARE SKILLS ARE REQUIRED FOR HEALTH WORKER STUDENTS TO ACHIEVE PROFICIENCY? LEARNING CURVE PATTERNS IN CHILD CARE SKILLS ACQUISITION.

    Science.gov (United States)

    Moghadam, Zahra Emami; Emami Zeydi, Amir; Mazlom, Seyed Reza; Abadi, Fatemeh Sardar; Pour, Parastoo Majidi; Davoudi, Malihe; Banafsheh, Elahe

    2015-10-01

    The vulnerability of children under 5 years old requires paying more attention to the health of this group. In the Iranian health care system, health workers are the first line of human resources for health care in rural areas. Because most health workers begin working in conditions with minimal facilities, their clinical qualifications are crucial. The aim of this study was to determine the number of repetitions of child care skills, required for health worker students to achieve proficiency based on the learning curve. A time series research design was used. Participants in this study were first year health worker students enrolled in three health schools in 2011. Data were collected using a questionnaire consisting of demographic information and a checklist evaluating the health worker students' clinical skills proficiency for child care. Data were analyzed using SPSS version 16.0 software (SPSS Inc., Chicago, IL) using descriptive and inferential statistics including Kruskal-Wallis and Pearson correlation coefficient tests. Learning curve patterns in child care skills acquisition showed that for less than 20 and between 20 to 29 times, the level of skill acquisition had an upward slope. Between 30- 39 the learning curve was descending, however the slope became ascending once more and then it leveled off (with change of less than 5%). It seems that 40 repetitions of child care skills are sufficient for health worker students to achieve proficiency. This suggests that time, resources and additional costs for training health worker students' trainees can be saved by this level of repetition.

  6. A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

    Directory of Open Access Journals (Sweden)

    Bernhard A. Kaplan

    2014-02-01

    Full Text Available Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB, and three types of cortical cells in the piriform cortex (PC. Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility to use a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tuftedcells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewiseorganized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

  7. A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system.

    Science.gov (United States)

    Kaplan, Bernhard A; Lansner, Anders

    2014-01-01

    Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

  8. Robust spike-train learning in spike-event based weight update.

    Science.gov (United States)

    Shrestha, Sumit Bam; Song, Qing

    2017-09-12

    Supervised learning algorithms in a spiking neural network either learn a spike-train pattern for a single neuron receiving input spike-train from multiple input synapses or learn to output the first spike time in a feedforward network setting. In this paper, we build upon spike-event based weight update strategy to learn continuous spike-train in a spiking neural network with a hidden layer using a dead zone on-off based adaptive learning rate rule which ensures convergence of the learning process in the sense of weight convergence and robustness of the learning process to external disturbances. Based on different benchmark problems, we compare this new method with other relevant spike-train learning algorithms. The results show that the speed of learning is much improved and the rate of successful learning is also greatly improved. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Iterative learning control algorithm for spiking behavior of neuron model

    Science.gov (United States)

    Li, Shunan; Li, Donghui; Wang, Jiang; Yu, Haitao

    2016-11-01

    Controlling neurons to generate a desired or normal spiking behavior is the fundamental building block of the treatment of many neurologic diseases. The objective of this work is to develop a novel control method-closed-loop proportional integral (PI)-type iterative learning control (ILC) algorithm to control the spiking behavior in model neurons. In order to verify the feasibility and effectiveness of the proposed method, two single-compartment standard models of different neuronal excitability are specifically considered: Hodgkin-Huxley (HH) model for class 1 neural excitability and Morris-Lecar (ML) model for class 2 neural excitability. ILC has remarkable advantages for the repetitive processes in nature. To further highlight the superiority of the proposed method, the performances of the iterative learning controller are compared to those of classical PI controller. Either in the classical PI control or in the PI control combined with ILC, appropriate background noises are added in neuron models to approach the problem under more realistic biophysical conditions. Simulation results show that the controller performances are more favorable when ILC is considered, no matter which neuronal excitability the neuron belongs to and no matter what kind of firing pattern the desired trajectory belongs to. The error between real and desired output is much smaller under ILC control signal, which suggests ILC of neuron’s spiking behavior is more accurate.

  10. PySpike - A Python library for analyzing spike train synchrony

    CERN Document Server

    Mulansky, Mario

    2016-01-01

    Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute bi- and multivariate dissimilarity profiles, averaged values and bivariate matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.

  11. Linking investment spikes and productivity growth

    NARCIS (Netherlands)

    Geylani, P.C.; Stefanou, S.E.

    2013-01-01

    We investigate the relationship between productivity growth and investment spikes using Census Bureau’s plant-level dataset for the U.S. food manufacturing industry. There are differences in productivity growth and investment spike patterns across different sub-industries and food manufacturing indu

  12. Variability in spatio-temporal pattern of trapezius activity and coordination of hand-arm muscles during a sustained repetitive dynamic task

    DEFF Research Database (Denmark)

    Samani, Afshin; Srinivasan, Divya; Mathiassen, Svend Erik;

    2016-01-01

    The spatio-temporal distribution of muscle activity has been suggested to be a determinant of fatigue development. Pursuing this hypothesis, we investigated the pattern of muscular activity in the shoulder and arm during a repetitive dynamic task performed until participants' rating of perceived...... power frequency (MNF) were calculated for all EMG signals. The barycenter of RMS values over the HD-EMG grid was also determined, as well as normalized mutual information (NMI) for each pair of muscles. Cycle-to-cycle variability of these metrics was also assessed. With time, EMG RMS increased for most...... of the muscles, and MNF decreased. Trapezius activity became higher on the lateral side than on the medial side of the HD-EMG grid and the barycenter moved in a lateral direction. NMI between muscle pairs increased with time while its variability decreased. The variability of the metrics during the initial 10...

  13. Supervised Learning in Multilayer Spiking Neural Networks

    CERN Document Server

    Sporea, Ioana

    2012-01-01

    The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

  14. The effect of repetition of infrequent familiar and unfamiliar visual patterns on components of the event-related brain potential.

    NARCIS (Netherlands)

    A. Kok; H. de Looren de Jong

    1980-01-01

    Examined changes in the waveforms of the event-related brain potential (ERP) during repeated presentations of infrequent-familiar and infrequent-unfamiliar visual patterns; Ss were 12 male university students. The EEG waveforms were averaged separately for each presentation of the 2 types of stimuli

  15. The ionic mechanism of gamma resonance in rat striatal fast-spiking neurons

    OpenAIRE

    Sciamanna, Giuseppe; Wilson, Charles J.

    2011-01-01

    Striatal fast-spiking (FS) cells in slices fire in the gamma frequency range and in vivo are often phase-locked to gamma oscillations in the field potential. We studied the firing patterns of these cells in slices from rats ages 16–23 days to determine the mechanism of their gamma resonance. The resonance of striatal FS cells was manifested as a minimum frequency for repetitive firing. At rheobase, cells fired a doublet of action potentials or doublets separated by pauses, with an instantaneo...

  16. Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

    CERN Document Server

    Grüning, André

    2011-01-01

    Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can be successfully applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons. We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons. While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understand...

  17. Improved SpikeProp for Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Falah Y. H. Ahmed

    2013-01-01

    Full Text Available A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures.

  18. Spike generation estimated from stationary spike trains in a variety of neurons in vivo.

    Science.gov (United States)

    Spanne, Anton; Geborek, Pontus; Bengtsson, Fredrik; Jörntell, Henrik

    2014-01-01

    To any model of brain function, the variability of neuronal spike firing is a problem that needs to be taken into account. Whereas the synaptic integration can be described in terms of the original Hodgkin-Huxley (H-H) formulations of conductance-based electrical signaling, the transformation of the resulting membrane potential into patterns of spike output is subjected to stochasticity that may not be captured with standard single neuron H-H models. The dynamics of the spike output is dependent on the normal background synaptic noise present in vivo, but the neuronal spike firing variability in vivo is not well studied. In the present study, we made long-term whole cell patch clamp recordings of stationary spike firing states across a range of membrane potentials from a variety of subcortical neurons in the non-anesthetized, decerebrated state in vivo. Based on the data, we formulated a simple, phenomenological model of the properties of the spike generation in each neuron that accurately captured the stationary spike firing statistics across all membrane potentials. The model consists of a parametric relationship between the mean and standard deviation of the inter-spike intervals, where the parameter is linearly related to the injected current over the membrane. This enabled it to generate accurate approximations of spike firing also under inhomogeneous conditions with input that varies over time. The parameters describing the spike firing statistics for different neuron types overlapped extensively, suggesting that the spike generation had similar properties across neurons.

  19. Spike generation estimated from stationary spike trains in a variety of neurons in vivo

    Directory of Open Access Journals (Sweden)

    Anton eSpanne

    2014-07-01

    Full Text Available To any model of brain function, the variability of neuronal spike firing is a problem that needs to be taken into account. Whereas the synaptic integration can be described in terms of the original Hodgkin-Huxley (H-H formulations of conductance-based electrical signaling, the transformation of the resulting membrane potential into patterns of spike output is subjected to stochasticity that may not be captured with standard single neuron H-H models. The dynamics of the spike output is dependent on the normal background synaptic noise present in vivo, but the neuronal spike firing variability in vivo is not well studied. In the present study, we made long-term whole cell patch clamp recordings of stationary spike firing states across a range of membrane potentials from a variety of subcortical neurons in the non-anesthetized, decerebrated state in vivo. Based on the data, we formulated a simple, phenomenological model of the properties of the spike generation in each neuron that accurately captured the stationary spike firing statistics across all membrane potentials. The model consists of a parametric relationship between the mean and standard deviation of the inter-spike intervals, where the parameter is linearly related to the injected current over the membrane. This enabled it to generate accurate approximations of spike firing also under inhomogeneous conditions with input that varies over time. The parameters describing the spike firing statistics for different neuron types overlapped extensively, suggesting that the spike generation had similar properties across neurons.

  20. The ionic mechanism of gamma resonance in rat striatal fast-spiking neurons.

    Science.gov (United States)

    Sciamanna, Giuseppe; Wilson, Charles J

    2011-12-01

    Striatal fast-spiking (FS) cells in slices fire in the gamma frequency range and in vivo are often phase-locked to gamma oscillations in the field potential. We studied the firing patterns of these cells in slices from rats ages 16-23 days to determine the mechanism of their gamma resonance. The resonance of striatal FS cells was manifested as a minimum frequency for repetitive firing. At rheobase, cells fired a doublet of action potentials or doublets separated by pauses, with an instantaneous firing rate averaging 44 spikes/s. The minimum rate for sustained firing was also responsible for the stuttering firing pattern. Firing rate adapted during each episode of firing, and bursts were terminated when firing was reduced to the minimum sustainable rate. Resonance and stuttering continued after blockade of Kv3 current using tetraethylammonium (0.1-1 mM). Both gamma resonance and stuttering were strongly dependent on Kv1 current. Blockade of Kv1 channels with dendrotoxin-I (100 nM) completely abolished the stuttering firing pattern, greatly lowered the minimum firing rate, abolished gamma-band subthreshold oscillations, and slowed spike frequency adaptation. The loss of resonance could be accounted for by a reduction in potassium current near spike threshold and the emergence of a fixed spike threshold. Inactivation of the Kv1 channel combined with the minimum firing rate could account for the stuttering firing pattern. The resonant properties conferred by this channel were shown to be adequate to account for their phase-locking to gamma-frequency inputs as seen in vivo.

  1. Patterns of rDNA and telomeric sequences diversification: contribution to repetitive DNA organization in Phyllostomidae bats.

    Science.gov (United States)

    Calixto, Merilane da Silva; de Andrade, Izaquiel Santos; Cabral-de-Mello, Diogo Cavalcanti; Santos, Neide; Martins, Cesar; Loreto, Vilma; de Souza, Maria José

    2014-02-01

    Chromosomal organization and the evolution of genome architecture can be investigated by physical mapping of the genes for 45S and 5S ribosomal DNAs (rDNAs) and by the analysis of telomeric sequences. We studied 12 species of bats belonging to four subfamilies of the family Phyllostomidae in order to correlate patterns of distribution of heterochromatin and the multigene families for rDNA. The number of clusters for 45S gene ranged from one to three pairs, with exclusively location in autosomes, except for Carollia perspicillata that had in X chromosome. The 5S gene all the species studied had only one site located on an autosomal pair. In no species the 45S and 5S genes collocated. The fluorescence in situ hybridization (FISH) probe for telomeric sequences revealed fluorescence on all telomeres in all species, except in Carollia perspicillata. Non-telomeric sites in the pericentromeric region of the chromosomes were observed in most species, ranged from one to 12 pairs. Most interstitial telomeric sequences were coincident with heterochromatic regions. The results obtained in the present work indicate that different evolutionary mechanisms are acting in Phyllostomidae genome architecture, as well as the occurrence of Robertsonian fusion during the chromosomal evolution of bats without a loss of telomeric sequences. These data contribute to understanding the organization of multigene families and telomeric sequences on bat genome as well as the chromosomal evolutionary history of Phyllostomidae bats.

  2. Characterizing the complexity of spontaneous motor unit patterns of amyotrophic lateral sclerosis using approximate entropy

    Science.gov (United States)

    Zhou, Ping; Barkhaus, Paul E.; Zhang, Xu; Zev Rymer, William

    2011-10-01

    This paper presents a novel application of the approximate entropy (ApEn) measurement for characterizing spontaneous motor unit activity of amyotrophic lateral sclerosis (ALS) patients. High-density surface electromyography (EMG) was used to record spontaneous motor unit activity bilaterally from the thenar muscles of nine ALS subjects. Three distinct patterns of spontaneous motor unit activity (sporadic spikes, tonic spikes and high-frequency repetitive spikes) were observed. For each pattern, complexity was characterized by calculating the ApEn values of the representative signal segments. A sliding window over each segment was also introduced to quantify the dynamic changes in complexity for the different spontaneous motor unit patterns. We found that the ApEn values for the sporadic spikes were the highest, while those of the high-frequency repetitive spikes were the lowest. There is a significant difference in mean ApEn values between two arbitrary groups of the three spontaneous motor unit patterns (P < 0.001). The dynamic ApEn curve from the sliding window analysis is capable of tracking variations in EMG activity, thus providing a vivid, distinctive description for different patterns of spontaneous motor unit action potentials in terms of their complexity. These findings expand the existing knowledge of spontaneous motor unit activity in ALS beyond what was previously obtained using conventional linear methods such as firing rate or inter-spike interval statistics.

  3. Stochastic Variational Learning in Recurrent Spiking Networks

    Directory of Open Access Journals (Sweden)

    Danilo eJimenez Rezende

    2014-04-01

    Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  4. Stochastic variational learning in recurrent spiking networks.

    Science.gov (United States)

    Jimenez Rezende, Danilo; Gerstner, Wulfram

    2014-01-01

    The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  5. Variability in spatio-temporal pattern of trapezius activity and coordination of hand-arm muscles during a sustained repetitive dynamic task.

    Science.gov (United States)

    Samani, Afshin; Srinivasan, Divya; Mathiassen, Svend Erik; Madeleine, Pascal

    2017-02-01

    The spatio-temporal distribution of muscle activity has been suggested to be a determinant of fatigue development. Pursuing this hypothesis, we investigated the pattern of muscular activity in the shoulder and arm during a repetitive dynamic task performed until participants' rating of perceived exertion reached 8 on Borg's CR-10 scale. We collected high-density surface electromyogram (HD-EMG) over the upper trapezius, as well as bipolar EMG from biceps brachii, triceps brachii, deltoideus anterior, serratus anterior, upper and lower trapezius from 21 healthy women. Root-mean-square (RMS) and mean power frequency (MNF) were calculated for all EMG signals. The barycenter of RMS values over the HD-EMG grid was also determined, as well as normalized mutual information (NMI) for each pair of muscles. Cycle-to-cycle variability of these metrics was also assessed. With time, EMG RMS increased for most of the muscles, and MNF decreased. Trapezius activity became higher on the lateral side than on the medial side of the HD-EMG grid and the barycenter moved in a lateral direction. NMI between muscle pairs increased with time while its variability decreased. The variability of the metrics during the initial 10 % of task performance was not associated with the time to task termination. Our results suggest that the considerable variability in force and posture contained in the dynamic task per se masks any possible effects of differences between subjects in initial motor variability on the rate of fatigue development.

  6. Analyzing multiple spike trains with nonparametric Granger causality.

    Science.gov (United States)

    Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou

    2009-08-01

    Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

  7. Spiking irregularity and frequency modulate the behavioral report of single-neuron stimulation.

    Science.gov (United States)

    Doron, Guy; von Heimendahl, Moritz; Schlattmann, Peter; Houweling, Arthur R; Brecht, Michael

    2014-02-01

    The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning.

  8. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

    Al-Shedivat, Maruan

    2015-06-01

    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.

  9. Leaders and followers: quantifying consistency in spatio-temporal propagation patterns

    Science.gov (United States)

    Kreuz, Thomas; Satuvuori, Eero; Pofahl, Martin; Mulansky, Mario

    2017-04-01

    Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a synfire pattern. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed SPIKE-order and spike train order, that define the synfire indicator value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neuroscience (giant depolarized potentials in mice slices) and climatology (El Niño sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality.

  10. Solar Decameter Spikes

    Science.gov (United States)

    Melnik, V. N.; Shevchuk, N. V.; Konovalenko, A. A.; Rucker, H. O.; Dorovskyy, V. V.; Poedts, S.; Lecacheux, A.

    2014-05-01

    We analyze and discuss the properties of decameter spikes observed in July - August 2002 by the UTR-2 radio telescope. These bursts have a short duration (about one second) and occur in a narrow frequency bandwidth (50 - 70 kHz). They are chaotically located in the dynamic spectrum. Decameter spikes are weak bursts: their fluxes do not exceed 200 - 300 s.f.u. An interesting feature of these spikes is the observed linear increase of the frequency bandwidth with frequency. This dependence can be explained in the framework of the plasma mechanism that causes the radio emission, taking into account that Langmuir waves are generated by fast electrons within a narrow angle θ≈13∘ - 18∘ along the direction of the electron propagation. In the present article we consider the problem of the short lifetime of decameter spikes and discuss why electrons generate plasma waves in limited regions.

  11. Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke.

    Science.gov (United States)

    Saleh, Soha; Fluet, Gerard; Qiu, Qinyin; Merians, Alma; Adamovich, Sergei V; Tunik, Eugene

    2017-01-01

    Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP), robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels-which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR) rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI) data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training) in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC), contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1), ipsilesional ventral premotor area (iPMv), and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT), in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD signal in multiple

  12. Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke

    Directory of Open Access Journals (Sweden)

    Soha Saleh

    2017-09-01

    Full Text Available Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP, robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels—which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC, contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1, ipsilesional ventral premotor area (iPMv, and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT, in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD

  13. Autocrine Signaling Underlies Fast Repetitive Plasma Membrane Translocation of Conventional and Novel Protein Kinase C Isoforms in β Cells.

    Science.gov (United States)

    Wuttke, Anne; Yu, Qian; Tengholm, Anders

    2016-07-15

    PKC signaling has been implicated in the regulation of many cell functions, including metabolism, cell death, proliferation, and secretion. Activation of conventional and novel PKC isoforms is associated with their Ca(2+)- and/or diacylglycerol (DAG)-dependent translocation to the plasma membrane. In β cells, exocytosis of insulin granules evokes brief (<10 s) local DAG elevations ("spiking") at the plasma membrane because of autocrine activation of P2Y1 purinoceptors by ATP co-released with insulin. Using total internal reflection microscopy, fluorescent protein-tagged PKCs, and signaling biosensors, we investigated whether DAG spiking causes membrane recruitment of PKCs and whether different classes of PKCs show characteristic responses. Glucose stimulation of MIN6 cells triggered DAG spiking with concomitant repetitive translocation of the novel isoforms PKCδ, PKCϵ, and PKCη. The conventional PKCα, PKCβI, and PKCβII isoforms showed a more complex pattern with both rapid and slow translocation. K(+) depolarization-induced PKCϵ translocation entirely mirrored DAG spiking, whereas PKCβI translocation showed a sustained component, reflecting the subplasma membrane Ca(2+) concentration ([Ca(2+)]pm), with additional effect during DAG spikes. Interference with DAG spiking by purinoceptor inhibition prevented intermittent translocation of PKCs and reduced insulin secretion but did not affect [Ca(2+)]pm elevation or sustained PKCβI translocation. The muscarinic agonist carbachol induced pronounced transient PKCβI translocation and sustained recruitment of PKCϵ. When rise of [Ca(2+)]pm was prevented, the carbachol-induced DAG and PKCϵ responses were somewhat reduced, but PKCβI translocation was completely abolished. We conclude that exocytosis-induced DAG spikes efficiently recruit both conventional and novel PKCs to the β cell plasma membrane. PKC signaling is thus implicated in autocrine regulation of β cell function.

  14. Studying spike trains using a van Rossum metric with a synapse-like filter.

    Science.gov (United States)

    Houghton, Conor

    2009-02-01

    Spike trains are unreliable. For example, in the primary sensory areas, spike patterns and precise spike times will vary between responses to the same stimulus. Nonetheless, information about sensory inputs is communicated in the form of spike trains. A challenge in understanding spike trains is to assess the significance of individual spikes in encoding information. One approach is to define a spike train metric, allowing a distance to be calculated between pairs of spike trains. In a good metric, this distance will depend on the information the spike trains encode. This method has been used previously to calculate the timescale over which the precision of spike times is significant. Here, a new metric is constructed based on a simple model of synaptic conductances which includes binding site depletion. Including binding site depletion in the metric means that a given individual spike has a smaller effect on the distance if it occurs soon after other spikes. The metric proves effective at classifying neuronal responses by stimuli in the sample data set of electro-physiological recordings from the primary auditory area of the zebra finch fore-brain. This shows that this is an effective metric for these spike trains suggesting that in these spike trains the significance of a spike is modulated by its proximity to previous spikes. This modulation is a putative information-coding property of spike trains.

  15. Detection of bursts in neuronal spike trains by the mean inter-spike interval method

    Institute of Scientific and Technical Information of China (English)

    Lin Chen; Yong Deng; Weihua Luo; Zhen Wang; Shaoqun Zeng

    2009-01-01

    Bursts are electrical spikes firing with a high frequency, which are the most important property in synaptic plasticity and information processing in the central nervous system. However, bursts are difficult to identify because bursting activities or patterns vary with phys-iological conditions or external stimuli. In this paper, a simple method automatically to detect bursts in spike trains is described. This method auto-adaptively sets a parameter (mean inter-spike interval) according to intrinsic properties of the detected burst spike trains, without any arbitrary choices or any operator judgrnent. When the mean value of several successive inter-spike intervals is not larger than the parameter, a burst is identified. By this method, bursts can be automatically extracted from different bursting patterns of cultured neurons on multi-electrode arrays, as accurately as by visual inspection. Furthermore, significant changes of burst variables caused by electrical stimulus have been found in spontaneous activity of neuronal network. These suggest that the mean inter-spike interval method is robust for detecting changes in burst patterns and characteristics induced by environmental alterations.

  16. Finding the event structure of neuronal spike trains.

    Science.gov (United States)

    Toups, J Vincent; Fellous, Jean-Marc; Thomas, Peter J; Sejnowski, Terrence J; Tiesinga, Paul H

    2011-09-01

    Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004 ). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.

  17. Repetitive firing properties of medial pontine reticular formation neurones of the rat recorded in vitro.

    Science.gov (United States)

    Gerber, U; Greene, R W; McCarley, R W

    1989-03-01

    1. Intracellularly recorded neurones in nucleus reticularis pontis caudalis of the medial pontine reticular formation (mPRF) in the in vitro slice preparation were analysed for repetitive firing properties in response to intracellularly applied constant-current pulses. 2. Three neuronal classes were defined by this procedure: (1) non-burst neurones, which had only a non-burst firing pattern; (2) low-threshold burst neurones, which had either a low-threshold burst pattern or a non-burst pattern; (3) high-threshold burst neurones, which had either a high-threshold burst pattern or a non-burst pattern. 3. Histological characterization of electrophysiologically identified mPRF neurones with carboxyfluorescein showed no definite morphological difference between the first two classes. There was a trend for low-threshold burst neurones to have larger somata. 4. The low-threshold burst was generated by a slow calcium-dependent low-threshold spike, revealed in the presence of tetrodotoxin. The size of the low-threshold spike and thus the number of fast action potentials in the low-threshold burst was controlled by at least five factors including: activation; inactivation; amplitude of low-threshold conductance available to be activated; delayed outward conductance; and early transient outward conductance. 5. The non-burst pattern examined in both non-burst and low-threshold burst neurones appeared to be controlled primarily by one or more calcium-dependent potassium conductances sensitive to the removal of calcium and tetraethyl-ammonium. In the presence of tetrodotoxin (TTX), the addition of antagonists to calcium-dependent potassium current revealed a slow high-threshold calcium spike which was distinguished from the low-threshold spike by its threshold, lack of inactivation (at potentials negative to -40 mV) and insensitivity to Mg2+. A long-duration after-hyperpolarization (greater than 0.5 s) was not observed in any of these cells. 6. An early transient outward

  18. Spike-based population coding and working memory.

    Directory of Open Access Journals (Sweden)

    Martin Boerlin

    2011-02-01

    Full Text Available Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environment. We propose that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons. As a result, these networks can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons. Importantly, we propose that population responses and persistent working memory states represent entire probability distributions and not only single stimulus values. These memories are reflected by sustained, asynchronous patterns of activity which make relevant information available to downstream neurons within their short time window of integration. Model neurons act as predictive encoders, only firing spikes which account for new information that has not yet been signaled. Thus, spike times signal deterministically a prediction error, contrary to rate codes in which spike times are considered to be random samples of an underlying firing rate. As a consequence of this coding scheme, a multitude of spike patterns can reliably encode the same information. This results in weakly correlated, Poisson-like spike trains that are sensitive to initial conditions but robust to even high levels of external neural noise. This spike train variability reproduces the one observed in cortical sensory spike trains, but cannot be equated to noise. On the contrary, it is a consequence of optimal spike-based inference. In contrast, we show that rate-based models perform poorly when implemented with stochastically spiking neurons.

  19. Matrix Metalloprotease 3 Activity Supports Hippocampal EPSP-to-Spike Plasticity Following Patterned Neuronal Activity via the Regulation of NMDAR Function and Calcium Flux.

    Science.gov (United States)

    Brzdąk, Patrycja; Włodarczyk, Jakub; Mozrzymas, Jerzy W; Wójtowicz, Tomasz

    2017-01-01

    Matrix metalloproteases (MMPs) comprise a family of endopeptidases that are involved in remodeling the extracellular matrix and play a critical role in learning and memory. At least 24 different MMP subtypes have been identified in the human brain, but less is known about the subtype-specific actions of MMP on neuronal plasticity. The long-term potentiation (LTP) of excitatory synaptic transmission and scaling of dendritic and somatic neuronal excitability are considered substrates of memory storage. We previously found that MMP-3 and MMP-2/9 may be differentially involved in shaping the induction and expression of excitatory postsynaptic potential (EPSP)-to-spike (E-S) potentiation in hippocampal brain slices. MMP-3 and MMP-2/9 proteolysis was previously shown to affect the integrity or mobility of synaptic N-methyl-D-aspartate receptors (NMDARs) in vitro. However, the functional outcome of such MMP-NMDAR interactions remains largely unknown. The present study investigated the role of these MMP subtypes in E-S plasticity and NMDAR function in mouse hippocampal acute brain slices. The temporal requirement for MMP-3/NMDAR activity in E-S potentiation within the CA1 field largely overlapped, and MMP-3 but not MMP-2/9 activity was crucial for the gain-of-function of NMDARs following LTP induction. Functional changes in E-S plasticity following MMP-3 inhibition largely correlated with the expression of cFos protein, a marker of activity-related gene transcription. Recombinant MMP-3 promoted a gain in NMDAR-mediated field potentials and somatodendritic Ca(2+) waves. These results suggest that long-term hippocampal E-S potentiation requires transient MMP-3 activity that promotes NMDAR-mediated postsynaptic Ca(2+) entry that is vital for the activation of downstream signaling cascades and gene transcription.

  20. Maturation of firing pattern in chick vestibular nucleus neurons.

    Science.gov (United States)

    Shao, M; Hirsch, J C; Peusner, K D

    2006-08-25

    The principal cells of the chick tangential nucleus are vestibular nucleus neurons participating in the vestibuloocular and vestibulocollic reflexes. In birds and mammals, spontaneous and stimulus-evoked firing of action potentials is essential for vestibular nucleus neurons to generate mature vestibular reflex activity. The emergence of spike-firing pattern and the underlying ion channels were studied in morphologically-identified principal cells using whole-cell patch-clamp recordings from brain slices of late-term embryos (embryonic day 16) and hatchling chickens (hatching day 1 and hatching day 5). Spontaneous spike activity emerged around the perinatal period, since at embryonic day 16 none of the principal cells generated spontaneous action potentials. However, at hatching day 1, 50% of the cells fired spontaneously (range, 3 to 32 spikes/s), which depended on synaptic transmission in most cells. By hatching day 5, 80% of the principal cells could fire action potentials spontaneously (range, 5 to 80 spikes/s), and this activity was independent of synaptic transmission and showed faster kinetics than at hatching day 1. Repetitive firing in response to depolarizing pulses appeared in the principal cells starting around embryonic day 16, when calcium-dependent potassium current modulated both the spontaneous and evoked spike firing activity. Altogether, these in vitro studies showed that during the perinatal period, the principal cells switched from displaying no spontaneous spike activity at resting membrane potential and generating one spike on depolarization to the tonic firing of spontaneous and evoked action potentials.

  1. Spectral components of cytosolic [Ca2+] spiking in neurons

    DEFF Research Database (Denmark)

    Kardos, J; Szilágyi, N; Juhász, G

    1998-01-01

    . Delayed complex responses of large [Ca2+]c spiking observed in cells from a different set of cultures were synthesized by a set of frequencies within the range 0.018-0.117 Hz. Differential frequency patterns are suggested as characteristics of the [Ca2+]c spiking responses of neurons under different...

  2. Comparison of electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil.

    Science.gov (United States)

    Ottosen, Lisbeth M; Lepkova, Katarina; Kubal, Martin

    2006-09-01

    Electrokinetic remediation methods for removal of heavy metals from polluted soils have been subjected for quite intense research during the past years since these methods are well suitable for fine-grained soils where other remediation methods fail. Electrodialytic remediation is an electrokinetic remediation method which is based on applying an electric dc field and the use of ion exchange membranes that ensures the main transport of heavy metals to be out of the pollutes soil. An experimental investigation was made with electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil under the same operational conditions (constant current density 0.2 mA/cm(2) and duration 28 days). The results of the present paper show that caution must be taken when generalising results obtained in spiked kaolinite to remediation of industrially polluted soils, as it was shown that the removal rate was higher in kaolinite than in both spiked soil and industrial polluted soil. The duration of spiking was found to be an important factor too, when attempting to relate remediation of spiked soil or kaolinite to remediation of industrially polluted soils. Spiking for 2 days was too short. However, spiking for 30 days resulted in a pattern that was more similar to that of industrially polluted soils with similar compositions both regarding sequential extraction and electrodialytic remediation result, though the remediation still progressed slightly faster in the spiked soil. Generalisation of remediation results to a variety of soil types must on the other hand be done with caution since the remediation results of different industrially polluted soils were very different. In one soil a total of 76% Cu was removed and in another soil no Cu was removed only redistributed within the soil. The factor with the highest influence on removal success was soil pH, which must be low in order to mobilize Cu, and thus the buffering capacity against acidification was

  3. Ion channel density and threshold dynamics of repetitive firing in a cortical neuron model.

    Science.gov (United States)

    Arhem, Peter; Blomberg, Clas

    2007-01-01

    Modifying the density and distribution of ion channels in a neuron (by natural up- and down-regulation, by pharmacological intervention or by spontaneous mutations) changes its activity pattern. In the present investigation, we analyze how the impulse patterns are regulated by the density of voltage-gated channels in a model neuron, based on voltage clamp measurements of hippocampal interneurons. At least three distinct oscillatory patterns, associated with three distinct regions in the Na-K channel density plane, were found. A stability analysis showed that the different regions are characterized by saddle-node, double-orbit, and Hopf bifurcation threshold dynamics, respectively. Single strongly graded action potentials occur in an area outside the oscillatory regions, but less graded action potentials occur together with repetitive firing over a considerable range of channel densities. The presently found relationship between channel densities and oscillatory behavior may be relevance for understanding principal spiking patterns of cortical neurons (regular firing and fast spiking). It may also be of relevance for understanding the action of pharmacological compounds on brain oscillatory activity.

  4. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

  5. Implementing Signature Neural Networks with Spiking Neurons

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the

  6. Autocrine Signaling Underlies Fast Repetitive Plasma Membrane Translocation of Conventional and Novel Protein Kinase C Isoforms in β Cells*

    Science.gov (United States)

    Wuttke, Anne; Yu, Qian; Tengholm, Anders

    2016-01-01

    PKC signaling has been implicated in the regulation of many cell functions, including metabolism, cell death, proliferation, and secretion. Activation of conventional and novel PKC isoforms is associated with their Ca2+- and/or diacylglycerol (DAG)-dependent translocation to the plasma membrane. In β cells, exocytosis of insulin granules evokes brief (<10 s) local DAG elevations (“spiking”) at the plasma membrane because of autocrine activation of P2Y1 purinoceptors by ATP co-released with insulin. Using total internal reflection microscopy, fluorescent protein-tagged PKCs, and signaling biosensors, we investigated whether DAG spiking causes membrane recruitment of PKCs and whether different classes of PKCs show characteristic responses. Glucose stimulation of MIN6 cells triggered DAG spiking with concomitant repetitive translocation of the novel isoforms PKCδ, PKCϵ, and PKCη. The conventional PKCα, PKCβI, and PKCβII isoforms showed a more complex pattern with both rapid and slow translocation. K+ depolarization-induced PKCϵ translocation entirely mirrored DAG spiking, whereas PKCβI translocation showed a sustained component, reflecting the subplasma membrane Ca2+ concentration ([Ca2+]pm), with additional effect during DAG spikes. Interference with DAG spiking by purinoceptor inhibition prevented intermittent translocation of PKCs and reduced insulin secretion but did not affect [Ca2+]pm elevation or sustained PKCβI translocation. The muscarinic agonist carbachol induced pronounced transient PKCβI translocation and sustained recruitment of PKCϵ. When rise of [Ca2+]pm was prevented, the carbachol-induced DAG and PKCϵ responses were somewhat reduced, but PKCβI translocation was completely abolished. We conclude that exocytosis-induced DAG spikes efficiently recruit both conventional and novel PKCs to the β cell plasma membrane. PKC signaling is thus implicated in autocrine regulation of β cell function. PMID:27226533

  7. A phonetic approach to consonant repetition in early words.

    Science.gov (United States)

    Kim, Namhee; Davis, Barbara L

    2015-08-01

    The goal of this study was to evaluate movement-based principles for understanding early speech output patterns. Consonant repetition patterns within children's actual productions of word forms were analyzed using spontaneous speech data from 10 typically developing American-English learning children between 12 and 36 months of age. Place of articulation, word level patterns, and developmental trends in CVC and CVCV repeated word forms were evaluated. Labial and coronal place repetitions dominated. Regressive repetition (e.g., [gag] for "dog") occurred frequently in CVC but not in CVCV word forms. Consonant repetition decreased over time. However, the children produced sound types available reported as being within young children's production system capabilities in consonant repetitions in all time periods. Findings suggest that a movement-based approach can provide a framework for comprehensively characterizing consonant place repetition patterns in early speech development.

  8. Nonsmooth dynamics in spiking neuron models

    Science.gov (United States)

    Coombes, S.; Thul, R.; Wedgwood, K. C. A.

    2012-11-01

    Large scale studies of spiking neural networks are a key part of modern approaches to understanding the dynamics of biological neural tissue. One approach in computational neuroscience has been to consider the detailed electrophysiological properties of neurons and build vast computational compartmental models. An alternative has been to develop minimal models of spiking neurons with a reduction in the dimensionality of both parameter and variable space that facilitates more effective simulation studies. In this latter case the single neuron model of choice is often a variant of the classic integrate-and-fire model, which is described by a nonsmooth dynamical system. In this paper we review some of the more popular spiking models of this class and describe the types of spiking pattern that they can generate (ranging from tonic to burst firing). We show that a number of techniques originally developed for the study of impact oscillators are directly relevant to their analysis, particularly those for treating grazing bifurcations. Importantly we highlight one particular single neuron model, capable of generating realistic spike trains, that is both computationally cheap and analytically tractable. This is a planar nonlinear integrate-and-fire model with a piecewise linear vector field and a state dependent reset upon spiking. We call this the PWL-IF model and analyse it at both the single neuron and network level. The techniques and terminology of nonsmooth dynamical systems are used to flesh out the bifurcation structure of the single neuron model, as well as to develop the notion of Lyapunov exponents. We also show how to construct the phase response curve for this system, emphasising that techniques in mathematical neuroscience may also translate back to the field of nonsmooth dynamical systems. The stability of periodic spiking orbits is assessed using a linear stability analysis of spiking times. At the network level we consider linear coupling between voltage

  9. Targeted capture sequencing in whitebark pine reveals range-wide demographic and adaptive patterns despite challenges of a large, repetitive genome

    Directory of Open Access Journals (Sweden)

    John eSyring

    2016-04-01

    Full Text Available Whitebark pine (Pinus albicaulis inhabits an expansive range in western North America, and it is a keystone species of subalpine environments. Whitebark is susceptible to multiple threats – climate change, white pine blister rust, mountain pine beetle, and fire exclusion – and it is suffering significant mortality range-wide, prompting the tree to be listed as ‘globally endangered’ by the International Union for Conservation of Nature (IUCN and ‘endangered’ by the Canadian government. Conservation collections (in situ and ex situ are being initiated to preserve the genetic legacy of the species. Reliable, transferrable, and highly variable genetic markers are essential for quantifying the genetic profiles of seed collections relative to natural stands, and ensuring the completeness of conservation collections. We evaluated the use of hybridization-based target capture to enrich specific genomic regions from the 30+ GB genome of whitebark pine, and to evaluate genetic variation across loci, trees, and geography. Probes were designed to capture 7,849 distinct genes, and screening was performed on 48 trees. Despite the inclusion of repetitive elements in the probe pool, the resulting dataset provided information on 4,452 genes and 32% of targeted positions (528,873 bp, and we were able to identify 12,390 segregating sites from 47 trees. Variations reveal strong geographic trends in heterozygosity and allelic richness, with trees from the southern Cascade and Sierra Range showing the greatest distinctiveness and differentiation. Our results show that even under non-optimal conditions (low enrichment efficiency; inclusion of repetitive elements in baits, targeted enrichment produces high quality, codominant genotypes from large genomes. The resulting data can be readily integrated into management and gene conservation activities for whitebark pine, and have the potential to be applied to other members of 5-needle pine group (Pinus subsect

  10. Spike-timing theory of working memory.

    Directory of Open Access Journals (Sweden)

    Botond Szatmáry

    Full Text Available Working memory (WM is the part of the brain's memory system that provides temporary storage and manipulation of information necessary for cognition. Although WM has limited capacity at any given time, it has vast memory content in the sense that it acts on the brain's nearly infinite repertoire of lifetime long-term memories. Using simulations, we show that large memory content and WM functionality emerge spontaneously if we take the spike-timing nature of neuronal processing into account. Here, memories are represented by extensively overlapping groups of neurons that exhibit stereotypical time-locked spatiotemporal spike-timing patterns, called polychronous patterns; and synapses forming such polychronous neuronal groups (PNGs are subject to associative synaptic plasticity in the form of both long-term and short-term spike-timing dependent plasticity. While long-term potentiation is essential in PNG formation, we show how short-term plasticity can temporarily strengthen the synapses of selected PNGs and lead to an increase in the spontaneous reactivation rate of these PNGs. This increased reactivation rate, consistent with in vivo recordings during WM tasks, results in high interspike interval variability and irregular, yet systematically changing, elevated firing rate profiles within the neurons of the selected PNGs. Additionally, our theory explains the relationship between such slowly changing firing rates and precisely timed spikes, and it reveals a novel relationship between WM and the perception of time on the order of seconds.

  11. Grammatical Change through Repetition.

    Science.gov (United States)

    Arevart, Supot

    1989-01-01

    The effect of repetition on grammatical change in an unrehearsed talk is examined based on a case study of a single learner. It was found that repetition allows for accuracy monitoring in that errors committed in repeated contexts undergo correction. Implications for teaching are discussed. (23 references) (LB)

  12. The Negative Repetition Effect

    Science.gov (United States)

    Mulligan, Neil W.; Peterson, Daniel J.

    2013-01-01

    A fundamental property of human memory is that repetition enhances memory. Peterson and Mulligan (2012) recently documented a surprising "negative repetition effect," in which participants who studied a list of cue-target pairs twice recalled fewer targets than a group who studied the pairs only once. Words within a pair rhymed, and…

  13. Roles of repetitive sequences

    Energy Technology Data Exchange (ETDEWEB)

    Bell, G.I.

    1991-12-31

    The DNA of higher eukaryotes contains many repetitive sequences. The study of repetitive sequences is important, not only because many have important biological function, but also because they provide information on genome organization, evolution and dynamics. In this paper, I will first discuss some generic effects that repetitive sequences will have upon genome dynamics and evolution. In particular, it will be shown that repetitive sequences foster recombination among, and turnover of, the elements of a genome. I will then consider some examples of repetitive sequences, notably minisatellite sequences and telomere sequences as examples of tandem repeats, without and with respectively known function, and Alu sequences as an example of interspersed repeats. Some other examples will also be considered in less detail.

  14. Roles of repetitive sequences

    Energy Technology Data Exchange (ETDEWEB)

    Bell, G.I.

    1991-12-31

    The DNA of higher eukaryotes contains many repetitive sequences. The study of repetitive sequences is important, not only because many have important biological function, but also because they provide information on genome organization, evolution and dynamics. In this paper, I will first discuss some generic effects that repetitive sequences will have upon genome dynamics and evolution. In particular, it will be shown that repetitive sequences foster recombination among, and turnover of, the elements of a genome. I will then consider some examples of repetitive sequences, notably minisatellite sequences and telomere sequences as examples of tandem repeats, without and with respectively known function, and Alu sequences as an example of interspersed repeats. Some other examples will also be considered in less detail.

  15. Implementing Signature Neural Networks with Spiking Neurons

    Directory of Open Access Journals (Sweden)

    José Luis Carrillo-Medina

    2016-12-01

    Full Text Available Spiking Neural Networks constitute the most promising approach to develop realistic ArtificialNeural Networks (ANNs. Unlike traditional firing rate-based paradigms, information coding inspiking models is based on the precise timing of individual spikes. Spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition. In recent years, majorbreakthroughs in neuroscience research have discovered new relevant computational principles indifferent living neural systems. Could ANNs benefit from some of these recent findings providingnovel elements of inspiration? This is an intriguing question and the development of spiking ANNsincluding novel bio-inspired information coding and processing strategies is gaining attention. Fromthis perspective, in this work, we adapt the core concepts of the recently proposed SignatureNeural Network paradigm – i.e., neural signatures to identify each unit in the network, localinformation contextualization during the processing and multicoding strategies for informationpropagation regarding the origin and the content of the data – to be employed in a spiking neuralnetwork. To the best of our knowledge, none of these mechanisms have been used yet in thecontext of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicabilityin such networks. Computer simulations show that a simple network model like the discussed hereexhibits complex self-organizing properties. The combination of multiple simultaneous encodingschemes allows the network to generate coexisting spatio-temporal patterns of activity encodinginformation in different spatio-temporal spaces. As a function of the network and/or intra-unitparameters shaping the corresponding encoding modality, different forms of competition amongthe evoked patterns can emerge even in the absence of inhibitory connections. These parametersalso

  16. Radioxenon spiked air.

    Science.gov (United States)

    Watrous, Matthew G; Delmore, James E; Hague, Robert K; Houghton, Tracy P; Jenson, Douglas D; Mann, Nick R

    2015-12-01

    Four of the radioactive xenon isotopes ((131m)Xe, (133m)Xe, (133)Xe and (135)Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The International Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This paper focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities.

  17. Spike sorting for polytrodes: a divide and conquer approach

    Directory of Open Access Journals (Sweden)

    Nicholas V. Swindale

    2014-02-01

    Full Text Available In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 minutes. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis. Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scaleable to larger multi-electrode arrays (MEAs.

  18. Trialogue: Preparation, Repetition and...

    Science.gov (United States)

    Oberg, Antoinette; And Others

    1996-01-01

    This paper interrogates both curriculum theory and the limits and potentials of textual forms. A set of overlapping discourses (a trialogue) focuses on inquiring into the roles of obsession and repetition in creating deeply interpretive locations for understanding. (SM)

  19. Identification of pre-spike network in patients with mesial temporal lobe epilepsy

    Directory of Open Access Journals (Sweden)

    Nahla L Faizo

    2014-10-01

    Full Text Available Background: Seizures and inter-ictal spikes in mesial temporal lobe epilepsy (MTLE affect a network of brain regions rather than a single epileptic focus. Simultaneous EEG-fMRI studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate functional connectivity changes immediately prior to the appearance of inter-ictal spikes on EEG in MTLE patients.Methods/principal findings: 15 MTLE patients underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10s epochs were defined relative to spike onset: spike (0s to 10s, pre-spike (-10s to 0s and rest (-20s to -10s, with no previous spikes in the preceding 45s. Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for functional connectivity analysis in the three conditions. A significant change in functional connectivity patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states.Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric hippocampal inhibition that may predispose to spike generation.

  20. Spectral components of cytosolic [Ca2+] spiking in neurons

    DEFF Research Database (Denmark)

    Kardos, J; Szilágyi, N; Juhász, G;

    1998-01-01

    into evolutionary spectra of a characteristic set of frequencies. Non-delayed small spikes on top of sustained [Ca2+]c were synthesized by a main component frequency, 0.132+/-0.012 Hz, showing its maximal amplitude in phase with the start of depolarization (25 mM KCI) combined with caffeine (10 mM) application....... Delayed complex responses of large [Ca2+]c spiking observed in cells from a different set of cultures were synthesized by a set of frequencies within the range 0.018-0.117 Hz. Differential frequency patterns are suggested as characteristics of the [Ca2+]c spiking responses of neurons under different...

  1. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  2. Learning probabilistic models of connectivity from multiple spike train data

    OpenAIRE

    2010-01-01

    Neuronal circuits or cell assemblies carry out brain function through complex coordinated firing patterns [1]. Inferring topology of neuronal circuits from simultaneously recorded spike train data is a challenging problem in neuroscience. In this work we present a new class of dynamic Bayesian networks to infer polysynaptic excitatory connectivity between spiking cortical neurons [2]. The emphasis on excitatory networks allows us to learn connectivity models by exploiting fast data mining alg...

  3. Clustering predicts memory performance in networks of spiking and non-spiking neurons

    Directory of Open Access Journals (Sweden)

    Weiliang eChen

    2011-03-01

    Full Text Available The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

  4. Coronavirus spike-receptor interactions

    NARCIS (Netherlands)

    Mou, H.

    2015-01-01

    Coronaviruses cause important diseases in humans and animals. Coronavirus infection starts with the virus binding with its spike proteins to molecules present on the surface of host cells that act as receptors. This spike-receptor interaction is highly specific and determines the virus’ cell, tissue

  5. Millisecond precision spike timing shapes tactile perception.

    Science.gov (United States)

    Mackevicius, Emily L; Best, Matthew D; Saal, Hannes P; Bensmaia, Sliman J

    2012-10-31

    In primates, the sense of touch has traditionally been considered to be a spatial modality, drawing an analogy to the visual system. In this view, stimuli are encoded in spatial patterns of activity over the sheet of receptors embedded in the skin. We propose that the spatial processing mode is complemented by a temporal one. Indeed, the transduction and processing of complex, high-frequency skin vibrations have been shown to play an important role in tactile texture perception, and the frequency composition of vibrations shapes the evoked percept. Mechanoreceptive afferents innervating the glabrous skin exhibit temporal patterning in their responses, but the importance and behavioral relevance of spike timing, particularly for naturalistic stimuli, remains to be elucidated. Based on neurophysiological recordings from Rhesus macaques, we show that spike timing conveys information about the frequency composition of skin vibrations, both for individual afferents and for afferent populations, and that the temporal fidelity varies across afferent class. Furthermore, the perception of skin vibrations, measured in human subjects, is better predicted when spike timing is taken into account, and the resolution that predicts perception best matches the optimal resolution of the respective afferent classes. In light of these results, the peripheral representation of complex skin vibrations draws a powerful analogy with the auditory and vibrissal systems.

  6. PySpike—A Python library for analyzing spike train synchrony

    Directory of Open Access Journals (Sweden)

    Mario Mulansky

    2016-01-01

    Full Text Available Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute similarity and dissimilarity profiles, averaged values and distance matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.

  7. Mapping spikes to sensations

    Directory of Open Access Journals (Sweden)

    Maik Christopher Stüttgen

    2011-11-01

    Full Text Available Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data is observed in parallel with spike trains in sensory neurons (the neurometric data. Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code’s performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of neural candidate codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research.

  8. Rayleigh--Taylor spike evaporation

    Energy Technology Data Exchange (ETDEWEB)

    Schappert, G. T.; Batha, S. H.; Klare, K. A.; Hollowell, D. E.; Mason, R. J.

    2001-09-01

    Laser-based experiments have shown that Rayleigh--Taylor (RT) growth in thin, perturbed copper foils leads to a phase dominated by narrow spikes between thin bubbles. These experiments were well modeled and diagnosed until this '' spike'' phase, but not into this spike phase. Experiments were designed, modeled, and performed on the OMEGA laser [T. R. Boehly, D. L. Brown, R. S. Craxton , Opt. Commun. 133, 495 (1997)] to study the late-time spike phase. To simulate the conditions and evolution of late time RT, a copper target was fabricated consisting of a series of thin ridges (spikes in cross section) 150 {mu}m apart on a thin flat copper backing. The target was placed on the side of a scale-1.2 hohlraum with the ridges pointing into the hohlraum, which was heated to 190 eV. Side-on radiography imaged the evolution of the ridges and flat copper backing into the typical RT bubble and spike structure including the '' mushroom-like feet'' on the tips of the spikes. RAGE computer models [R. M. Baltrusaitis, M. L. Gittings, R. P. Weaver, R. F. Benjamin, and J. M. Budzinski, Phys. Fluids 8, 2471 (1996)] show the formation of the '' mushrooms,'' as well as how the backing material converges to lengthen the spike. The computer predictions of evolving spike and bubble lengths match measurements fairly well for the thicker backing targets but not for the thinner backings.

  9. On the Functions of Lexical Repetition in English Texts

    Institute of Scientific and Technical Information of China (English)

    XIAO Fuliang

    2016-01-01

    Lexical repetition, as a cohesive device of an English text, can help make up a cohesive and coherent text. Therefore, in English textual learning, it is helpful for students to know about different patterns and functions of lexical repetition to improve their English level and ability.

  10. Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation.

    Science.gov (United States)

    Cyr, André; Boukadoum, Mounir

    2013-03-01

    This paper presents a novel bio-inspired habituation function for robots under control by an artificial spiking neural network. This non-associative learning rule is modelled at the synaptic level and validated through robotic behaviours in reaction to different stimuli patterns in a dynamical virtual 3D world. Habituation is minimally represented to show an attenuated response after exposure to and perception of persistent external stimuli. Based on current neurosciences research, the originality of this rule includes modulated response to variable frequencies of the captured stimuli. Filtering out repetitive data from the natural habituation mechanism has been demonstrated to be a key factor in the attention phenomenon, and inserting such a rule operating at multiple temporal dimensions of stimuli increases a robot's adaptive behaviours by ignoring broader contextual irrelevant information.

  11. Chromosomal distribution patterns of the (AC)10 microsatellite and other repetitive sequences, and their use in chromosome rearrangement analysis of species of the genus Avena.

    Science.gov (United States)

    Fominaya, Araceli; Loarce, Yolanda; Montes, Alexander; Ferrer, Esther

    2017-03-01

    Fluorescence in situ hybridization (FISH) was used to determine the physical location of the (AC)10 microsatellite in metaphase chromosomes of six diploid species (AA or CC genomes), two tetraploid species (AACC genome), and five cultivars of two hexaploid species (AACCDD genome) of the genus Avena, a genus in which genomic relationships remain obscure. A preferential distribution of the (AC)10 microsatellite in the pericentromeric and interstitial regions was seen in both the A- and D-genome chromosomes, while in C-genome chromosomes the majority of signals were located in the pericentromeric heterochromatic regions. New large chromosome rearrangements were detected in two polyploid species: an intergenomic translocation involving chromosomes 17AL and 21DS in Avena sativa 'Araceli' and another involving chromosomes 4CL and 21DS in the analyzed cultivars of Avena byzantina. The latter 4CL-21DS intergenomic translocation differentiates clearly between A. sativa and A. byzantina. Searches for common hybridization patterns on the chromosomes of different species revealed chromosome 10A of Avena magna and 21D of hexaploid oats to be very similar in terms of the distribution of 45S and Am1 sequences. This suggests a common origin for these chromosomes and supports a CCDD rather than an AACC genomic designation for this species.

  12. Mechanism of frequency-dependent broadening of molluscan neurone soma spikes.

    Science.gov (United States)

    Aldrich, R W; Getting, P A; Thompson, S H

    1979-06-01

    1. Action potentials recorded from isolated dorid neurone somata increase in duration, i.e. broaden, during low frequency repetitive firing. Spike broadening is substantially reduced by external Co ions and implicates an inward Ca current. 2. During repetitive voltage clamp steps at frequencies slower than 1 Hz, in 100 mM-tetraethyl ammonium ions (TEA) inward Ca currents do not increase in amplitude. 3. Repetitive action potentials result in inactivation of delayed outward current. Likewise, repetitive voltage clamp steps which cause inactivation of delayed outward current also result in longer duration action potentials. 4. The frequency dependence of spike broadening and inactivation of the voltage dependent component (IK) of delayed outward current are similar. 5. Inactivation of IK is observed in all cells, however, only cells with relative large inward Ca currents show significant spike broadening. Spike broadening apparently results from the frequency dependent inactivation of IK which increases the expression of inward Ca current as a prominent shoulder on the repolarizing phase of the action potential. In addition, the presence of a prolonged Ca current increases the duration of the first action potential thereby allowing sufficient time for inactivation of IK.

  13. Contrast Adaptation Decreases Complexity in Retinal Ganglion Cell Spike Train

    Institute of Scientific and Technical Information of China (English)

    WANG Guang-Li; HUANG Shi-Yong; ZHANG Ying-Ying; LIANG Pei-Ji

    2007-01-01

    @@ The difference in temporal structures of retinal ganglion cell spike trains between spontaneous activity and firing activity after contrast adaptation is investigated. The Lempel-Ziv complexity analysis reveals that the complexity of the neural spike train decreases after contrast adaptation. This implies that the behaviour of the neuron becomes ordered, which may carry relevant information about the external stimulus. Thus, during the neuron activity after contrast adaptation, external information could be encoded in forms of some certain patterns in the temporal structure of spike train that is significantly different, compared to that of the spike train during spontaneous activity, although the firing rates in spontaneous activity and firing activity after contrast adaptation are sometime similar.

  14. SPIKY: A graphical user interface for monitoring spike train synchrony

    CERN Document Server

    Bozanic, Nebojsa

    2014-01-01

    Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface which facilitates the applicati...

  15. Novel porcine repetitive elements

    Directory of Open Access Journals (Sweden)

    Nonneman Dan J

    2006-12-01

    Full Text Available Abstract Background Repetitive elements comprise ~45% of mammalian genomes and are increasingly known to impact genomic function by contributing to the genomic architecture, by direct regulation of gene expression and by affecting genomic size, diversity and evolution. The ubiquity and increasingly understood importance of repetitive elements contribute to the need to identify and annotate them. We set out to identify previously uncharacterized repetitive DNA in the porcine genome. Once found, we characterized the prevalence of these repeats in other mammals. Results We discovered 27 repetitive elements in 220 BACs covering 1% of the porcine genome (Comparative Vertebrate Sequencing Initiative; CVSI. These repeats varied in length from 55 to 1059 nucleotides. To estimate copy numbers, we went to an independent source of data, the BAC-end sequences (Wellcome Trust Sanger Institute, covering approximately 15% of the porcine genome. Copy numbers in BAC-ends were less than one hundred for 6 repeat elements, between 100 and 1000 for 16 and between 1,000 and 10,000 for 5. Several of the repeat elements were found in the bovine genome and we have identified two with orthologous sites, indicating that these elements were present in their common ancestor. None of the repeat elements were found in primate, rodent or dog genomes. We were unable to identify any of the replication machinery common to active transposable elements in these newly identified repeats. Conclusion The presence of both orthologous and non-orthologous sites indicates that some sites existed prior to speciation and some were generated later. The identification of low to moderate copy number repetitive DNA that is specific to artiodactyls will be critical in the assembly of livestock genomes and studies of comparative genomics.

  16. Upper limb biomechanics during the volleyball serve and spike.

    Science.gov (United States)

    Reeser, Jonathan C; Fleisig, Glenn S; Bolt, Becky; Ruan, Mianfang

    2010-09-01

    The shoulder is the third-most commonly injured body part in volleyball, with the majority of shoulder problems resulting from chronic overuse. Significant kinetic differences exist among specific types of volleyball serves and spikes. Controlled laboratory study. Fourteen healthy female collegiate volleyball players performed 5 successful trials of 4 skills: 2 directional spikes, an off-speed roll shot, and the float serve. Volunteers who were competent in jump serves (n, 5) performed 5 trials of that skill. A 240-Hz 3-dimensional automatic digitizing system captured each trial. Multivariate analysis of variance and post hoc paired t tests were used to compare kinetic parameters for the shoulder and elbow across all the skills (except the jump serve). A similar statistical analysis was performed for upper extremity kinematics. Forces, torques, and angular velocities at the shoulder and elbow were lowest for the roll shot and second-lowest for the float serve. No differences were detected between the cross-body and straight-ahead spikes. Although there was an insufficient number of participants to statistically analyze the jump serve, the data for it appear similar to those of the cross-body and straight-ahead spikes. Shoulder abduction at the instant of ball contact was approximately 130° for all skills, which is substantially greater than that previously reported for female athletes performing tennis serves or baseball pitches. Because shoulder kinetics were greatest during spiking, the volleyball player with symptoms of shoulder overuse may wish to reduce the number of repetitions performed during practice. Limiting the number of jump serves may also reduce the athlete's risk of overuse-related shoulder dysfunction. Volleyball-specific overhead skills, such as the spike and serve, produce considerable upper extremity force and torque, which may contribute to the risk of shoulder injury.

  17. Wavelet analysis of epileptic spikes

    CERN Document Server

    Latka, M; Kozik, A; West, B J; Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

    2003-01-01

    Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

  18. Wavelet analysis of epileptic spikes

    Science.gov (United States)

    Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

    2003-05-01

    Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

  19. Exact computation of the maximum-entropy potential of spiking neural-network models.

    Science.gov (United States)

    Cofré, R; Cessac, B

    2014-05-01

    Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in neural networks is a central question in computational neuroscience. The maximum-entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. However, in spite of good performance in terms of prediction, the fitting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuromimetic models) provide a probabilistic mapping between the stimulus, network architecture, and spike patterns in terms of conditional probabilities. In this paper we build an exact analytical mapping between neuromimetic and maximum-entropy models.

  20. Bayesian Inference for Structured Spike and Slab Priors

    DEFF Research Database (Denmark)

    Andersen, Michael Riis; Winther, Ole; Hansen, Lars Kai

    2014-01-01

    Sparse signal recovery addresses the problem of solving underdetermined linear inverse problems subject to a sparsity constraint. We propose a novel prior formulation, the structured spike and slab prior, which allows to incorporate a priori knowledge of the sparsity pattern by imposing a spatial...... Gaussian process on the spike and slab probabilities. Thus, prior information on the structure of the sparsity pattern can be encoded using generic covariance functions. Furthermore, we provide a Bayesian inference scheme for the proposed model based on the expectation propagation framework. Using...

  1. Bayesian Inference for Structured Spike and Slab Priors

    DEFF Research Database (Denmark)

    Andersen, Michael Riis; Winther, Ole; Hansen, Lars Kai

    2014-01-01

    Sparse signal recovery addresses the problem of solving underdetermined linear inverse problems subject to a sparsity constraint. We propose a novel prior formulation, the structured spike and slab prior, which allows to incorporate a priori knowledge of the sparsity pattern by imposing a spatial...... Gaussian process on the spike and slab probabilities. Thus, prior information on the structure of the sparsity pattern can be encoded using generic covariance functions. Furthermore, we provide a Bayesian inference scheme for the proposed model based on the expectation propagation framework. Using...

  2. Learning Precise Spike Train to Spike Train Transformations in Multilayer Feedforward Neuronal Networks

    OpenAIRE

    2014-01-01

    We derive a synaptic weight update rule for learning temporally precise spike train to spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed th...

  3. Improved patterning of ITO coated with gold masking layer on glass substrate using nanosecond fiber laser and etching

    Energy Technology Data Exchange (ETDEWEB)

    Tan, Nguyen Ngoc; Hung, Duong Thanh; Anh, Vo Tran [High Safety Vehicle Core Technology Research Center, Department of Mechanical & Automotive Engineering, Inje University, Gimhae (Korea, Republic of); BongChul, Kang, E-mail: kbc@kumoh.ac.kr [Department of Inteligent Mechanical Engineering, Kumoh National Institute of Technology, Gumi (Korea, Republic of); HyunChul, Kim, E-mail: mechkhc@inje.ac.kr [High Safety Vehicle Core Technology Research Center, Department of Mechanical & Automotive Engineering, Inje University, Gimhae (Korea, Republic of)

    2015-05-01

    Highlights: • A new patterning method for ITO thin film is introduced. • Gold thin film is important in decrease spikes formed in ITO patterning process. • The laser pulse width occupies a significant effect the patterning surface quality. • Etching process is the effective method to remove the spikes at rims of pattern. • A considerable improvement over patterning quality is obtained by proposed method. - Abstract: In this paper, an indium–tin oxide (ITO) thin-film patterning method for higher pattern quality and productivity compared to the short-pulsed laser direct writing method is presented. We sputtered a thin ITO layer on a glass substrate, and then, plated a thin gold layer onto the ITO layer. The combined structure of the three layers (glass–ITO–gold) was patterned using laser-induced plasma generated by an ytterbium pulsed fiber laser (λ = 1064 nm). The results showed that the process parameters of 50 mm/s in scanning speed, 14 ns pulse duration, and a repetition rate of 7.5 kHz represented optimum conditions for the fabrication of ITO channels. Under these conditions, a channel 23.4 μm wide and 20 nm deep was obtained. However, built-up spikes (∼15 nm in height) resulted in a decrease in channel quality, and consequently, short circuit occurred at some patterned positions. These built-up spikes were completely removed by dipping the ITO layer into an etchant (18 wt.% HCl). A gold masking layer on the ITO surface was found to increase the channel surface quality without any decrease in ITO thickness. Moreover, the effects of repetition rate, scanning speed, and etching characteristics on surface quality were investigated.

  4. Wavelet transform of neural spike trains

    Science.gov (United States)

    Kim, Youngtae; Jung, Min Whan; Kim, Yunbok

    2000-02-01

    Wavelet transform of neural spike trains recorded with a tetrode in the rat primary somatosensory cortex is described. Continuous wavelet transform (CWT) of the spike train clearly shows singularities hidden in the noisy or chaotic spike trains. A multiresolution analysis of the spike train is also carried out using discrete wavelet transform (DWT) for denoising and approximating at different time scales. Results suggest that this multiscale shape analysis can be a useful tool for classifying the spike trains.

  5. Is it clean or contaminated soil? Using petrogenic versus biogenic GC-FID chromatogram patterns to mathematically resolve false petroleum hydrocarbon detections in clean organic soils: a crude oil-spiked peat microcosm experiment.

    Science.gov (United States)

    Kelly-Hooper, Francine; Farwell, Andrea J; Pike, Glenna; Kennedy, Jocelyn; Wang, Zhendi; Grunsky, Eric C; Dixon, D George

    2013-10-01

    The Canadian Council of Ministers of the Environment (CCME) reference method for the Canada-wide standard (CWS) for petroleum hydrocarbon (PHC) in soil provides chemistry analysis standards and guidelines for the management of contaminated sites. However, these methods can coextract natural biogenic organic compounds (BOCs) from organic soils, causing false exceedences of toxicity guidelines. The present 300-d microcosm experiment used CWS PHC tier 1 soil extraction and gas chromatography-flame ionization detector (GC-FID) analysis to develop a new tier 2 mathematical approach to resolving this problem. Carbon fractions F2 (C10-C16), F3 (C16-C34), and F4 (>C34) as well as subfractions F3a (C16-C22) and F3b (C22-C34) were studied in peat and sand spiked once with Federated crude oil. These carbon ranges were also studied in 14 light to heavy crude oils. The F3 range in the clean peat was dominated by F3b, whereas the crude oils had approximately equal F3a and F3b distributions. The F2 was nondetectable in the clean peat but was a significant component in crude oil. The crude oil–spiked peat had elevated F2 and F3a distributions. The BOC-adjusted PHC F3 calculation estimated the true PHC concentrations in the spiked peat. The F2:F3b ratio of less than 0.10 indicated PHC absence in the clean peat, and the ratio of greater than or equal to 0.10 indicated PHC presence in the spiked peat and sand. Validation studies are required to confirm whether this new tier 2 approach is applicable to real-case scenarios. Potential adoption of this approach could minimize unnecessary ecological disruptions of thousands of peatlands throughout Canada while also saving millions of dollars in management costs.

  6. Reading spike timing without a clock: intrinsic decoding of spike trains.

    Science.gov (United States)

    Panzeri, Stefano; Ince, Robin A A; Diamond, Mathew E; Kayser, Christoph

    2014-03-05

    The precise timing of action potentials of sensory neurons relative to the time of stimulus presentation carries substantial sensory information that is lost or degraded when these responses are summed over longer time windows. However, it is unclear whether and how downstream networks can access information in precise time-varying neural responses. Here, we review approaches to test the hypothesis that the activity of neural populations provides the temporal reference frames needed to decode temporal spike patterns. These approaches are based on comparing the single-trial stimulus discriminability obtained from neural codes defined with respect to network-intrinsic reference frames to the discriminability obtained from codes defined relative to the experimenter's computer clock. Application of this formalism to auditory, visual and somatosensory data shows that information carried by millisecond-scale spike times can be decoded robustly even with little or no independent external knowledge of stimulus time. In cortex, key components of such intrinsic temporal reference frames include dedicated neural populations that signal stimulus onset with reliable and precise latencies, and low-frequency oscillations that can serve as reference for partitioning extended neuronal responses into informative spike patterns.

  7. Membrane currents of spiking cells isolated from turtle retina.

    Science.gov (United States)

    Lasater, E M; Witkovsky, P

    1990-05-01

    We examined the membrane properties of spiking neurons isolated from the turtle (Pseudemys scripta) retina. The cells were maintained in culture for 1-7 days and were studied with the whole cell patch clamp technique. We utilized cells whose perikaryal diameters were greater than 15 microns since Kolb (1982) reported that ganglion cell perikarya in Pseudemys retina are 13-25 microns, whereas amacrine perikarya are less than 14 microns in diameter. We identified 5 currents in the studied cells: (1) a transient sodium current (INa) blocked by TTX, (2) a sustained calcium current (ICa) blocked by cobalt and enhanced by Bay-K 8644, (3) a calcium-dependent potassium current (IK(Ca)), (4) an A-type transient potassium current (IA) somewhat more sensitive to 4-AP than TEA, (5) a sustained potassium current (IK) more sensitive to TEA than 4-AP. The estimated average input resistance of the cells at -70 mV was 720 +/- 440 M omega. When all active currents were blocked, the membrane resistance between -130 and +20 mV was 2.5 G omega. When examined under current clamp, some cells produced multiple spikes to depolarizing steps of 0.1-0.3 nA, whereas other cells produced only a single spike irrespective of the strength of the current pulse. Most single spikers had an outward current that rose to a peak relatively slowly, whereas multiple spikers tend to have a more rapidly activating outward current. Under current clamp, 4-AP slowed the repolarization phase of the spike thus broadening it, but did not always abolish the ability to produce multiple spikes. TEA induced a depolarized plateau following the initial spike which precluded further spikes. It thus appears that the spiking patterns of the retinal cells are shaped primarily by the kinetics of INa, IK and IA and to a lesser extent by IK(Ca).

  8. SPIKY: a graphical user interface for monitoring spike train synchrony.

    Science.gov (United States)

    Kreuz, Thomas; Mulansky, Mario; Bozanic, Nebojsa

    2015-05-01

    Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. Copyright © 2015 the American Physiological Society.

  9. Experimental observation of transition from chaotic bursting to chaotic spiking in a neural pacemaker.

    Science.gov (United States)

    Gu, Huaguang

    2013-06-01

    The transition from chaotic bursting to chaotic spiking has been simulated and analyzed in theoretical neuronal models. In the present study, we report experimental observations in a neural pacemaker of a transition from chaotic bursting to chaotic spiking within a bifurcation scenario from period-1 bursting to period-1 spiking. This was induced by adjusting extracellular calcium or potassium concentrations. The bifurcation scenario began from period-doubling bifurcations or period-adding sequences of bursting pattern. This chaotic bursting is characterized by alternations between multiple continuous spikes and a long duration of quiescence, whereas chaotic spiking is comprised of fast, continuous spikes without periods of quiescence. Chaotic bursting changed to chaotic spiking as long interspike intervals (ISIs) of quiescence disappeared within bursting patterns, drastically decreasing both ISIs and the magnitude of the chaotic attractors. Deterministic structures of the chaotic bursting and spiking patterns are also identified by a short-term prediction. The experimental observations, which agree with published findings in theoretical neuronal models, demonstrate the existence and reveal the dynamics of a neuronal transition from chaotic bursting to chaotic spiking in the nervous system.

  10. MIMICRY, DIFFERENCE AND REPETITION

    Directory of Open Access Journals (Sweden)

    Marcelo Mendes de Souza

    2008-07-01

    Full Text Available This article addresses Homi K. Bhabha’s concept of mimicry in a broader context, other than that of cultural studies and post-colonial studies, bringing together other concepts, such as that of Gilles Deleuze in Difference and repetition, among other texts, and other names, such as Silviano Santiago, Jorge Luís Borges, Franz Kafka and Giorgio Agamben. As a partial conclusion, the article intends to oppose Bhabha’s freudian-marxist view to Five propositions on Psychoanalysis (1973, Gilles Deleuze’s text about Psychoanalysis published right after his book The Anti-Oedipus.

  11. Neuronal Networks in Children with Continuous Spikes and Waves during Slow Sleep

    Science.gov (United States)

    Siniatchkin, Michael; Groening, Kristina; Moehring, Jan; Moeller, Friederike; Boor, Rainer; Brodbeck, Verena; Michel, Christoph M.; Rodionov, Roman; Lemieux, Louis; Stephani, Ulrich

    2010-01-01

    Epileptic encephalopathy with continuous spikes and waves during slow sleep is an age-related disorder characterized by the presence of interictal epileptiform discharges during at least greater than 85% of sleep and cognitive deficits associated with this electroencephalography pattern. The pathophysiological mechanisms of continuous spikes and…

  12. Transgenerational effects of environmental enrichment on repetitive motor behavior development.

    Science.gov (United States)

    Bechard, Allison R; Lewis, Mark H

    2016-07-01

    The favorable consequences of environmental enrichment (EE) on brain and behavior development are well documented. Much less is known, however, about transgenerational benefits of EE on non-enriched offspring. We explored whether transgenerational effects of EE might extend to the development of repetitive motor behaviors in deer mice. Repetitive motor behaviors are invariant patterns of movement that, across species, can be reduced by EE. We found that EE not only attenuated the development of repetitive behavior in dams, but also in their non-enriched offspring. Moreover, maternal behavior did not seem to mediate the transgenerational effect we found, although repetitive behavior was affected by reproductive experience. These data support a beneficial transgenerational effect of EE on repetitive behavior development and suggest a novel benefit of reproductive experience.

  13. Computing with Spiking Neuron Networks

    NARCIS (Netherlands)

    Paugam-Moisy, H.; Bohte, S.M.; Rozenberg, G.; Baeck, T.H.W.; Kok, J.N.

    2012-01-01

    Abstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd gener- ation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an ac- curate modeling of synaptic interactions between neu

  14. Forskolin suppresses delayed-rectifier K+ currents and enhances spike frequency-dependent adaptation of sympathetic neurons.

    Directory of Open Access Journals (Sweden)

    Luis I Angel-Chavez

    Full Text Available In signal transduction research natural or synthetic molecules are commonly used to target a great variety of signaling proteins. For instance, forskolin, a diterpene activator of adenylate cyclase, has been widely used in cellular preparations to increase the intracellular cAMP level. However, it has been shown that forskolin directly inhibits some cloned K+ channels, which in excitable cells set up the resting membrane potential, the shape of action potential and regulate repetitive firing. Despite the growing evidence indicating that K+ channels are blocked by forskolin, there are no studies yet assessing the impact of this mechanism of action on neuron excitability and firing patterns. In sympathetic neurons, we find that forskolin and its derivative 1,9-Dideoxyforskolin, reversibly suppress the delayed rectifier K+ current (IKV. Besides, forskolin reduced the spike afterhyperpolarization and enhanced the spike frequency-dependent adaptation. Given that IKV is mostly generated by Kv2.1 channels, HEK-293 cells were transfected with cDNA encoding for the Kv2.1 α subunit, to characterize the mechanism of forskolin action. Both drugs reversible suppressed the Kv2.1-mediated K+ currents. Forskolin inhibited Kv2.1 currents and IKV with an IC50 of ~32 μM and ~24 µM, respectively. Besides, the drug induced an apparent current inactivation and slowed-down current deactivation. We suggest that forskolin reduces the excitability of sympathetic neurons by enhancing the spike frequency-dependent adaptation, partially through a direct block of their native Kv2.1 channels.

  15. The power ratio and the interval map spiking models and extracellular data

    CERN Document Server

    Reich, D S; Knight, B W; Reich, Daniel S.; Victor, Jonathan D.; Knight, Bruce W.

    1998-01-01

    We describe a new, computationally simple method for analyzing the dynamics of neuronal spike trains driven by external stimuli. The goal of our method is to test the predictions of simple spike-generating models against extracellularly recorded neuronal responses. Through a new statistic called the power ratio, we distinguish between two broad classes of responses: (1) responses that can be completely characterized by a variable firing rate, (for example, modulated Poisson and gamma spike trains); and (2) responses for which firing rate variations alone are not sufficient to characterize response dynamics (for example, leaky integrate-and-fire spike trains as well as Poisson spike trains with long absolute refractory periods). We show that the responses of many visual neurons in the cat retinal ganglion, cat lateral geniculate nucleus, and macaque primary visual cortex fall into the second class, which implies that the pattern of spike times can carry significant information about visual stimuli. Our results...

  16. Upregulation of transmitter release probability improves a conversion of synaptic analogue signals into neuronal digital spikes

    Science.gov (United States)

    2012-01-01

    Action potentials at the neurons and graded signals at the synapses are primary codes in the brain. In terms of their functional interaction, the studies were focused on the influence of presynaptic spike patterns on synaptic activities. How the synapse dynamics quantitatively regulates the encoding of postsynaptic digital spikes remains unclear. We investigated this question at unitary glutamatergic synapses on cortical GABAergic neurons, especially the quantitative influences of release probability on synapse dynamics and neuronal encoding. Glutamate release probability and synaptic strength are proportionally upregulated by presynaptic sequential spikes. The upregulation of release probability and the efficiency of probability-driven synaptic facilitation are strengthened by elevating presynaptic spike frequency and Ca2+. The upregulation of release probability improves spike capacity and timing precision at postsynaptic neuron. These results suggest that the upregulation of presynaptic glutamate release facilitates a conversion of synaptic analogue signals into digital spikes in postsynaptic neurons, i.e., a functional compatibility between presynaptic and postsynaptic partners. PMID:22852823

  17. Local Variation of Hashtag Spike Trains and Popularity in Twitter.

    Science.gov (United States)

    Sanlı, Ceyda; Lambiotte, Renaud

    2015-01-01

    We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media.

  18. Repetition in Waiting for Godot

    Institute of Scientific and Technical Information of China (English)

    李想; 魏妍

    2015-01-01

    Waiting for Godot is one of the most famous plays written by Samuel Barclay Beckett, and also is the founding work of“Theatre of the Absurd”. In the drama, repetitive phenomena shed light on the whole construction considerably. All the charac-ters were helpless and unthinking. Their dialogues were simple, nonsense and repetitive. Two scenes were cyclical. Repetition was used subtly in order to express the theme of the play, showing mental crisis after depravation of WWII.

  19. Automatic spike sorting using tuning information.

    Science.gov (United States)

    Ventura, Valérie

    2009-09-01

    Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.

  20. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

    Science.gov (United States)

    Onken, Arno; Liu, Jian K; Karunasekara, P P Chamanthi R; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

    2016-11-01

    Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image

  1. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

    Directory of Open Access Journals (Sweden)

    Arno Onken

    2016-11-01

    Full Text Available Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations, in their temporal dimension (temporal neural response variations, or in their combination (temporally coordinated neural population firing. Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together, temporal firing patterns (temporal activation of these groups of neurons and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial. We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine

  2. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Christian Albers

    Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  3. Multiplexed Spike Coding and Adaptation in the Thalamus

    Directory of Open Access Journals (Sweden)

    Rebecca A. Mease

    2017-05-01

    Full Text Available High-frequency “burst” clusters of spikes are a generic output pattern of many neurons. While bursting is a ubiquitous computational feature of different nervous systems across animal species, the encoding of synaptic inputs by bursts is not well understood. We find that bursting neurons in the rodent thalamus employ “multiplexing” to differentially encode low- and high-frequency stimulus features associated with either T-type calcium “low-threshold” or fast sodium spiking events, respectively, and these events adapt differently. Thus, thalamic bursts encode disparate information in three channels: (1 burst size, (2 burst onset time, and (3 precise spike timing within bursts. Strikingly, this latter “intraburst” encoding channel shows millisecond-level feature selectivity and adapts across statistical contexts to maintain stable information encoded per spike. Consequently, calcium events both encode low-frequency stimuli and, in parallel, gate a transient window for high-frequency, adaptive stimulus encoding by sodium spike timing, allowing bursts to efficiently convey fine-scale temporal information.

  4. Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

    Directory of Open Access Journals (Sweden)

    Chao Huang

    2016-06-01

    Full Text Available Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.

  5. Experimental and modelling investigation of surface EMG spike analysis.

    Science.gov (United States)

    Gabriel, David A; Christie, Anita; Inglis, J Greig; Kamen, Gary

    2011-05-01

    A pattern classification method based on five measures extracted from the surface electromyographic (sEMG) signal is used to provide a unique characterization of the interference pattern for different motor unit behaviours. This study investigated the sensitivity of the five sEMG measures during the force gradation process. Tissue and electrode filtering effects were further evaluated using a sEMG model. Subjects (N=8) performed isometric elbow flexion contractions from 0 to 100% MVC. The sEMG signals from the biceps brachii were recorded simultaneously with force. The basic building block of the sEMG model was the detection of single fibre action potentials (SFAPs) through a homogeneous, equivalent isotropic, infinite volume conduction medium. The SFAPs were summed to generate single motor unit action potentials. The physiologic properties from a well-known muscle model and motor unit recruitment and firing rate schemes were combined to generate synthetic sEMG signals. The following pattern classification measures were calculated: mean spike amplitude, mean spike frequency, mean spike slope, mean spike duration, and the mean number of peaks per spike. Root-mean-square amplitude and mean power frequency were also calculated. Taken together, the experimental data and modelling analysis showed that below 50% MVC, the pattern classification measures were more sensitive to changes in force than traditional time and frequency measures. However, there are additional limitations associated with electrode distance from the source that must be explored further. Future experimental work should ensure that the inter-electrode distance is no greater than 1cm to mitigate the effects of tissue filtering.

  6. A simple algorithm for averaging spike trains.

    Science.gov (United States)

    Julienne, Hannah; Houghton, Conor

    2013-02-25

    Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains.

  7. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

    2016-01-01

    The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated fash...

  8. Automatic spikes detection in seismogram

    Institute of Scientific and Technical Information of China (English)

    王海军; 靳平; 刘贵忠

    2003-01-01

    @@ Data processing for seismic network is very complex and fussy, because a lot of data is recorded in seismic network every day, which make it impossible to process these data all by manual work. Therefore, seismic data should be processed automatically to produce a initial results about events detection and location. Afterwards, these results are reviewed and modified by analyst. In automatic processing data quality checking is important. There are three main problem data thatexist in real seismic records, which include: spike, repeated data and dropouts. Spike is defined as isolated large amplitude point; the other two problem datahave the same features that amplitude of sample points are uniform in a interval. In data quality checking, the first step is to detect and statistic problem data in a data segment, if percent of problem data exceed a threshold, then the whole data segment is masked and not be processed in the later process.

  9. Relationship between repetitive firing and afterhyperpolarizations in human neocortical neurons.

    Science.gov (United States)

    Lorenzon, N M; Foehring, R C

    1992-02-01

    1. Human neocortical neurons fire repetitively in response to long depolarizing current injections. The slope of the relationship between average firing frequency and injected current (f-I slope) was linear or bilinear in these cells. The mean steady-state f-I slope (average of the last 500 ms of a 1-s firing episode) was 57.8 Hz/nA. The instantaneous firing rate decreased with time during a 1-s constant-current injection (spike frequency adaptation). Also, human neurons exhibited habituation in response to a 1-s current stimulus repeated every 2 s. 2. Afterhyperpolarizations (AHPs) reflect the active ionic conductances after action potentials. We studied AHPs with the use of intracellular recordings and pharmacological manipulations in the in vitro slice preparation to 1) gain insight into the ionic mechanisms underlying the AHPs and 2) elucidate the role that the underlying currents play in the functional behavior of human cortical neurons. 3. We have classified three AHPs in human neocortical neurons on the basis of their time courses: fast, medium, and slow. The amplitude of the AHPs was dependent on stimulus intensity and duration, number and frequency of spikes, and membrane potential. 4. The fast AHP had a reversal potential of -65 mV and was eliminated in extracellular Co2+, tetraethylammonium (TEA) or 4-aminopyridine, and intracellular TEA or CsCl. These manipulations also caused an increase in spike width. 5. The medium AHP had a reversal potential of -90 to -93 mV (22-24 mV hyperpolarized from mean resting potential). This AHP was reduced by Co2+, apamin, tubocurare, muscarine, norepinephrine (NE), and serotonin (5-HT). Pharmacological manipulations suggest that the medium AHP is produced in part by 1) a Ca-dependent K+ current and 2) a time-dependent anomalous rectifier (IH). 6. The slow AHP reversed at -83 to -87 mV (14-18 mV hyperpolarized from mean resting potential). This AHP was diminished by Co2+, muscarine, NE, and 5-HT. The pharmacology of the

  10. Prospective Coding by Spiking Neurons.

    Directory of Open Access Journals (Sweden)

    Johanni Brea

    2016-06-01

    Full Text Available Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood. Here we propose a biologically plausible synaptic plasticity rule to learn predictions on a single neuron level on a timescale of seconds. The learning rule allows a spiking two-compartment neuron to match its current firing rate to its own expected future discounted firing rate. For instance, if an originally neutral event is repeatedly followed by an event that elevates the firing rate of a neuron, the originally neutral event will eventually also elevate the neuron's firing rate. The plasticity rule is a form of spike timing dependent plasticity in which a presynaptic spike followed by a postsynaptic spike leads to potentiation. Even if the plasticity window has a width of 20 milliseconds, associations on the time scale of seconds can be learned. We illustrate prospective coding with three examples: learning to predict a time varying input, learning to predict the next stimulus in a delayed paired-associate task and learning with a recurrent network to reproduce a temporally compressed version of a sequence. We discuss the potential role of the learning mechanism in classical trace conditioning. In the special case that the signal to be predicted encodes reward, the neuron learns to predict the discounted future reward and learning is closely related to the temporal difference learning algorithm TD(λ.

  11. Understanding maximal repetitions in strings

    CERN Document Server

    Crochemore, Maxime

    2008-01-01

    The cornerstone of any algorithm computing all repetitions in a string of length n in O(n) time is the fact that the number of runs (or maximal repetitions) is O(n). We give a simple proof of this result. As a consequence of our approach, the stronger result concerning the linearity of the sum of exponents of all runs follows easily.

  12. Measuring multiple spike train synchrony.

    Science.gov (United States)

    Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I

    2009-10-15

    Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

  13. Repertoires of spike avalanches are modulated by behavior and novelty

    Directory of Open Access Journals (Sweden)

    Tiago Lins Ribeiro

    2016-03-01

    Full Text Available Neuronal avalanches measured as consecutive bouts of thresholded field potentials represent a statistical signature that the brain operates near a critical point. In theory, criticality optimizes stimulus sensitivity, information transmission, computational capability and mnemonic repertoires size. Field potential avalanches recorded via multielectrode arrays from cortical slice cultures are repeatable spatiotemporal activity patterns. It remains unclear whether avalanches of action potentials observed in forebrain regions of freely-behaving rats also form recursive repertoires, and whether these have any behavioral relevance. Here we show that spike avalanches, recorded from hippocampus and sensory neocortex of freely-behaving rats, constitute distinct families of recursive spatiotemporal patterns. A significant number of those patterns were specific to a behavioral state. Although avalanches produced during sleep were mostly similar to others that occurred during waking, the repertoire of patterns recruited during sleep differed significantly from that of waking. More importantly, exposure to novel objects increased the rate at which new patterns arose, also leading to changes in post-exposure repertoires, which were significantly different from those before the exposure. A significant number of families occurred exclusively during periods of whisker contact with objects, but few were associated with specific objects. Altogether, the results provide original evidence linking behavior and criticality at the spike level: spike avalanches form repertoires that emerge in waking, recur during sleep, are diversified by novelty and contribute to object representation.

  14. Repertoires of Spike Avalanches Are Modulated by Behavior and Novelty.

    Science.gov (United States)

    Ribeiro, Tiago L; Ribeiro, Sidarta; Copelli, Mauro

    2016-01-01

    Neuronal avalanches measured as consecutive bouts of thresholded field potentials represent a statistical signature that the brain operates near a critical point. In theory, criticality optimizes stimulus sensitivity, information transmission, computational capability and mnemonic repertoires size. Field potential avalanches recorded via multielectrode arrays from cortical slice cultures are repeatable spatiotemporal activity patterns. It remains unclear whether avalanches of action potentials observed in forebrain regions of freely-behaving rats also form recursive repertoires, and whether these have any behavioral relevance. Here, we show that spike avalanches, recorded from hippocampus (HP) and sensory neocortex of freely-behaving rats, constitute distinct families of recursive spatiotemporal patterns. A significant number of those patterns were specific to a behavioral state. Although avalanches produced during sleep were mostly similar to others that occurred during waking, the repertoire of patterns recruited during sleep differed significantly from that of waking. More importantly, exposure to novel objects increased the rate at which new patterns arose, also leading to changes in post-exposure repertoires, which were significantly different from those before the exposure. A significant number of families occurred exclusively during periods of whisker contact with objects, but few were associated with specific objects. Altogether, the results provide original evidence linking behavior and criticality at the spike level: spike avalanches form repertoires that emerge in waking, recur during sleep, are diversified by novelty and contribute to object representation.

  15. Perceptual Repetition Blindness Effects

    Science.gov (United States)

    Hochhaus, Larry; Johnston, James C.; Null, Cynthia H. (Technical Monitor)

    1994-01-01

    The phenomenon of repetition blindness (RB) may reveal a new limitation on human perceptual processing. Recently, however, researchers have attributed RB to post-perceptual processes such as memory retrieval and/or reporting biases. The standard rapid serial visual presentation (RSVP) paradigm used in most RB studies is, indeed, open to such objections. Here we investigate RB using a "single-frame" paradigm introduced by Johnston and Hale (1984) in which memory demands are minimal. Subjects made only a single judgement about whether one masked target word was the same or different than a post-target probe. Confidence ratings permitted use of signal detection methods to assess sensitivity and bias effects. In the critical condition for RB a precue of the post-target word was provided prior to the target stimulus (identity precue), so that the required judgement amounted to whether the target did or did not repeat the precue word. In control treatments, the precue was either an unrelated word or a dummy.

  16. Detection of bursts and pauses in spike trains.

    Science.gov (United States)

    Ko, D; Wilson, C J; Lobb, C J; Paladini, C A

    2012-10-15

    Midbrain dopaminergic neurons in vivo exhibit a wide range of firing patterns. They normally fire constantly at a low rate, and speed up, firing a phasic burst when reward exceeds prediction, or pause when an expected reward does not occur. Therefore, the detection of bursts and pauses from spike train data is a critical problem when studying the role of phasic dopamine (DA) in reward related learning, and other DA dependent behaviors. However, few statistical methods have been developed that can identify bursts and pauses simultaneously. We propose a new statistical method, the Robust Gaussian Surprise (RGS) method, which performs an exhaustive search of bursts and pauses in spike trains simultaneously. We found that the RGS method is adaptable to various patterns of spike trains recorded in vivo, and is not influenced by baseline firing rate, making it applicable to all in vivo spike trains where baseline firing rates vary over time. We compare the performance of the RGS method to other methods of detecting bursts, such as the Poisson Surprise (PS), Rank Surprise (RS), and Template methods. Analysis of data using the RGS method reveals potential mechanisms underlying how bursts and pauses are controlled in DA neurons.

  17. The stochastic properties of input spike trains control neuronal arithmetic.

    Science.gov (United States)

    Bures, Zbynek

    2012-02-01

    In the nervous system, the representation of signals is based predominantly on the rate and timing of neuronal discharges. In most everyday tasks, the brain has to carry out a variety of mathematical operations on the discharge patterns. Recent findings show that even single neurons are capable of performing basic arithmetic on the sequences of spikes. However, the interaction of the two spike trains, and thus the resulting arithmetic operation may be influenced by the stochastic properties of the interacting spike trains. If we represent the individual discharges as events of a random point process, then an arithmetical operation is given by the interaction of two point processes. Employing a probabilistic model based on detection of coincidence of random events and complementary computer simulations, we show that the point process statistics control the arithmetical operation being performed and, particularly, that it is possible to switch from subtraction to division solely by changing the distribution of the inter-event intervals of the processes. Consequences of the model for evaluation of binaural information in the auditory brainstem are demonstrated. The results accentuate the importance of the stochastic properties of neuronal discharge patterns for information processing in the brain; further studies related to neuronal arithmetic should therefore consider the statistics of the interacting spike trains.

  18. Emergence of spike correlations in periodically forced excitable systems

    Science.gov (United States)

    Reinoso, José A.; Torrent, M. C.; Masoller, Cristina

    2016-09-01

    In sensory neurons the presence of noise can facilitate the detection of weak information-carrying signals, which are encoded and transmitted via correlated sequences of spikes. Here we investigate the relative temporal order in spike sequences induced by a subthreshold periodic input in the presence of white Gaussian noise. To simulate the spikes, we use the FitzHugh-Nagumo model and to investigate the output sequence of interspike intervals (ISIs), we use the symbolic method of ordinal analysis. We find different types of relative temporal order in the form of preferred ordinal patterns that depend on both the strength of the noise and the period of the input signal. We also demonstrate a resonancelike behavior, as certain periods and noise levels enhance temporal ordering in the ISI sequence, maximizing the probability of the preferred patterns. Our findings could be relevant for understanding the mechanisms underlying temporal coding, by which single sensory neurons represent in spike sequences the information about weak periodic stimuli.

  19. Spike-timing-based computation in sound localization.

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2010-11-01

    Full Text Available Spike timing is precise in the auditory system and it has been argued that it conveys information about auditory stimuli, in particular about the location of a sound source. However, beyond simple time differences, the way in which neurons might extract this information is unclear and the potential computational advantages are unknown. The computational difficulty of this task for an animal is to locate the source of an unexpected sound from two monaural signals that are highly dependent on the unknown source signal. In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. The model was able to accurately estimate the location of previously unknown sounds in both azimuth and elevation (including front/back discrimination in a known acoustic environment. We found that multiple representations of different acoustic environments could coexist as sets of overlapping neural assemblies which could be associated with spatial locations by Hebbian learning. The model demonstrates the computational relevance of relative spike timing to extract spatial information about sources independently of the source signal.

  20. Reliability of Spike Timing in Neocortical Neurons

    Science.gov (United States)

    Mainen, Zachary F.; Sejnowski, Terrence J.

    1995-06-01

    It is not known whether the variability of neural activity in the cerebral cortex carries information or reflects noisy underlying mechanisms. In an examination of the reliability of spike generation using recordings from neurons in rat neocortical slices, the precision of spike timing was found to depend on stimulus transients. Constant stimuli led to imprecise spike trains, whereas stimuli with fluctuations resembling synaptic activity produced spike trains with timing reproducible to less than 1 millisecond. These data suggest a low intrinsic noise level in spike generation, which could allow cortical neurons to accurately transform synaptic input into spike sequences, supporting a possible role for spike timing in the processing of cortical information by the neocortex.

  1. Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex.

    Directory of Open Access Journals (Sweden)

    Laureline Logiaco

    2015-08-01

    Full Text Available The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

  2. Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

    Science.gov (United States)

    Xue, Ming; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Yu, Hai-Tao; Chen, Ying-Yuan

    2013-09-01

    The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture (MA) manipulations are taken at the ‘Zusanli’ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval (ISI) statistical histogram is fitted by the gamma distribution, which has two parameters: one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate-independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture.

  3. Spike timing rigidity is maintained in bursting neurons under pentobarbital-induced anesthetic conditions

    Directory of Open Access Journals (Sweden)

    Risako Kato

    2016-11-01

    Full Text Available Pentobarbital potentiates γ-aminobutyric acid (GABA-mediated inhibitory synaptic transmission by prolonging the open time of GABAA receptors. However, it is unknown how pentobarbital regulates cortical neuronal activities via local circuits in vivo. To examine this question, we performed extracellular unit recording in rat insular cortex under awake and anesthetic conditions. Not a few studies apply time-rescaling theorem to detect the features of repetitive spike firing. Similar to these methods, we define an average spike interval locally in time using random matrix theory (RMT, which enables us to compare different activity states on a universal scale. Neurons with high spontaneous firing frequency (> 5 Hz and bursting were classified as HFB neurons (n = 10, and those with low spontaneous firing frequency (< 10 Hz and without bursting were classified as non-HFB neurons (n = 48. Pentobarbital injection (30 mg/kg reduced firing frequency in all HFB neurons and in 78% of non-HFB neurons. RMT analysis demonstrated that pentobarbital increased in the number of neurons with repulsion in both HFB and non-HFB neurons, suggesting that there is a correlation between spikes within a short interspike interval. Under awake conditions, in 50% of HFB and 40% of non-HFB neurons, the decay phase of normalized histograms of spontaneous firing were fitted to an exponential function, which indicated that the first spike had no correlation with subsequent spikes. In contrast, under pentobarbital-induced anesthesia conditions, the number of non-HFB neurons that were fitted to an exponential function increased to 80%, but almost no change in HFB neurons was observed. These results suggest that under both awake and pentobarbital-induced anesthetized conditions, spike firing in HFB neurons is more robustly regulated by preceding spikes than by non-HFB neurons, which may reflect the GABAA receptor-mediated regulation of cortical activities. Whole-cell patch

  4. Spike Timing Rigidity Is Maintained in Bursting Neurons under Pentobarbital-Induced Anesthetic Conditions

    Science.gov (United States)

    Kato, Risako; Yamanaka, Masanori; Yokota, Eiko; Koshikawa, Noriaki; Kobayashi, Masayuki

    2016-01-01

    Pentobarbital potentiates γ-aminobutyric acid (GABA)-mediated inhibitory synaptic transmission by prolonging the open time of GABAA receptors. However, it is unknown how pentobarbital regulates cortical neuronal activities via local circuits in vivo. To examine this question, we performed extracellular unit recording in rat insular cortex under awake and anesthetic conditions. Not a few studies apply time-rescaling theorem to detect the features of repetitive spike firing. Similar to these methods, we define an average spike interval locally in time using random matrix theory (RMT), which enables us to compare different activity states on a universal scale. Neurons with high spontaneous firing frequency (>5 Hz) and bursting were classified as HFB neurons (n = 10), and those with low spontaneous firing frequency (<10 Hz) and without bursting were classified as non-HFB neurons (n = 48). Pentobarbital injection (30 mg/kg) reduced firing frequency in all HFB neurons and in 78% of non-HFB neurons. RMT analysis demonstrated that pentobarbital increased in the number of neurons with repulsion in both HFB and non-HFB neurons, suggesting that there is a correlation between spikes within a short interspike interval (ISI). Under awake conditions, in 50% of HFB and 40% of non-HFB neurons, the decay phase of normalized histograms of spontaneous firing were fitted to an exponential function, which indicated that the first spike had no correlation with subsequent spikes. In contrast, under pentobarbital-induced anesthesia conditions, the number of non-HFB neurons that were fitted to an exponential function increased to 80%, but almost no change in HFB neurons was observed. These results suggest that under both awake and pentobarbital-induced anesthetized conditions, spike firing in HFB neurons is more robustly regulated by preceding spikes than by non-HFB neurons, which may reflect the GABAA receptor-mediated regulation of cortical activities. Whole-cell patch-clamp recording in

  5. An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation.

    Science.gov (United States)

    Wang, Runchun; Cohen, Gregory; Stiefel, Klaus M; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, André

    2013-01-01

    We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes.

  6. Multiscale analysis of neural spike trains.

    Science.gov (United States)

    Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin

    2014-01-30

    This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code.

  7. Modeling repetitive motions using structured light.

    Science.gov (United States)

    Xu, Yi; Aliaga, Daniel G

    2010-01-01

    Obtaining models of dynamic 3D objects is an important part of content generation for computer graphics. Numerous methods have been extended from static scenarios to model dynamic scenes. If the states or poses of the dynamic object repeat often during a sequence (but not necessarily periodically), we call such a repetitive motion. There are many objects, such as toys, machines, and humans, undergoing repetitive motions. Our key observation is that when a motion-state repeats, we can sample the scene under the same motion state again but using a different set of parameters; thus, providing more information of each motion state. This enables robustly acquiring dense 3D information difficult for objects with repetitive motions using only simple hardware. After the motion sequence, we group temporally disjoint observations of the same motion state together and produce a smooth space-time reconstruction of the scene. Effectively, the dynamic scene modeling problem is converted to a series of static scene reconstructions, which are easier to tackle. The varying sampling parameters can be, for example, structured-light patterns, illumination directions, and viewpoints resulting in different modeling techniques. Based on this observation, we present an image-based motion-state framework and demonstrate our paradigm using either a synchronized or an unsynchronized structured-light acquisition method.

  8. Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study

    Directory of Open Access Journals (Sweden)

    Jesus A Garrido

    2013-05-01

    Full Text Available The way long-term synaptic plasticity regulates neuronal spike patterns is not completely understood. This issue is especially relevant for the cerebellum, which is endowed with several forms of long-term synaptic plasticity and has been predicted to operate as a timing and a learning machine. Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. The weight at mossy fiber to granule cell synapses regulated the delay of the first spike and the weight at mossy fiber and parallel fiber to Golgi cell synapses regulated the duration of the time-window during which the first-spike could be emitted. Moreover, the weights of synapses controlling Golgi cell activation regulated the intensity of granule cell inhibition and therefore the number of spikes that could be emitted. First spike timing was regulated with millisecond precision and the number of spikes ranged from 0 to 3. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway.

  9. The Ripple Pond: Enabling Spiking Networks to See

    Directory of Open Access Journals (Sweden)

    Saeed eAfshar

    2013-11-01

    Full Text Available We present the biologically inspired Ripple Pond Network (RPN, a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilising the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable temporal patterns and the use of asynchronous frames for information binding.

  10. Challenges in the quantification and interpretation of spike-LFP relationships.

    Science.gov (United States)

    Ray, Supratim

    2015-04-01

    Brain signals often show fluctuations in particular frequency bands, which are highly conserved across species and are associated with specific behavioural states. Such rhythmic patterns can be captured in the local field potential (LFP), which is obtained by low-pass filtering the extracellular signal recorded from microelectrodes. However, LFP also captures other neural processes that are associated with spikes, such as synaptic events preceding a spike, low-frequency component of the action potential ("spike bleed-through") and spike afterhyperpolarization, which pose difficulties in the estimation of the amplitude and phase of the rhythm with respect to spikes. Here we discuss these issues and different techniques that have been used to dissociate the rhythm from other neural events in the LFP.

  11. Evolution of Plastic Learning in Spiking Networks via Memristive Connections

    OpenAIRE

    Howard, Gerard; Gale, Ella; Bull, Larry; Costello, Ben de Lacy; Adamatzky, Andy

    2012-01-01

    This article presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of (i) linear resistors (ii) constant-valued connections...

  12. Comparison of degradation between indigenous and spiked bisphenol A and triclosan in a biosolids amended soil

    Energy Technology Data Exchange (ETDEWEB)

    Langdon, Kate A., E-mail: Kate.Langdon@csiro.au [School of Agriculture, Food and Wine and Waite Research Institute, University of Adelaide, South Australia, 5005, Adelaide (Australia); Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia); Warne, Michael StJ. [Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia); Smernik, Ronald J. [School of Agriculture, Food and Wine and Waite Research Institute, University of Adelaide, South Australia, 5005, Adelaide (Australia); Shareef, Ali; Kookana, Rai S. [Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia)

    2013-03-01

    This study compared the degradation of indigenous bisphenol A (BPA) and triclosan (TCS) in a biosolids-amended soil, to the degradation of spiked labelled surrogates of the same compounds (BPA-d{sub 16} and TCS-{sup 13}C{sub 12}). The aim was to determine if spiking experiments accurately predict the degradation of compounds in biosolids-amended soils using two different types of biosolids, a centrifuge dried biosolids (CDB) and a lagoon dried biosolids (LDB). The rate of degradation of the compounds was examined and the results indicated that there were considerable differences between the indigenous and spiked compounds. These differences were more marked for BPA, for which the indigenous compound was detectable throughout the study, whereas the spiked compound decreased to below the detection limit prior to the study completion. The rate of degradation for the indigenous BPA was approximately 5-times slower than that of the spiked BPA-d{sub 16}. The indigenous and spiked TCS were both detectable throughout the study, however, the shape of the degradation curves varied considerably, particularly in the CDB treatment. These findings show that spiking experiments may not be suitable to predict the degradation and persistence of organic compounds following land application of biosolids. - Highlights: ► Degradation of indigenous and spiked compounds from biosolids were compared. ► Differences were observed for both the rate and pattern of degradation. ► Spiked bisphenol A entirely degraded however the indigenous compound remained. ► TCS was detectable during the experiment however the degradation patterns varied. ► Spiking experiments may not be suitable to predict degradation of organic compounds.

  13. A memristive spiking neuron with firing rate coding

    Directory of Open Access Journals (Sweden)

    Marina eIgnatov

    2015-10-01

    Full Text Available Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2 and on the chemical electromigration cell Ag/TiO2-x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

  14. Uncovering representations of sleep-associated hippocampal ensemble spike activity

    Science.gov (United States)

    Chen, Zhe; Grosmark, Andres D.; Penagos, Hector; Wilson, Matthew A.

    2016-08-01

    Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.

  15. Effects of phase on homeostatic spike rates.

    Science.gov (United States)

    Fisher, Nicholas; Talathi, Sachin S; Carney, Paul R; Ditto, William L

    2010-05-01

    Recent experimental results by Talathi et al. (Neurosci Lett 455:145-149, 2009) showed a divergence in the spike rates of two types of population spike events, representing the putative activity of the excitatory and inhibitory neurons in the CA1 area of an animal model for temporal lobe epilepsy. The divergence in the spike rate was accompanied by a shift in the phase of oscillations between these spike rates leading to a spontaneous epileptic seizure. In this study, we propose a model of homeostatic synaptic plasticity which assumes that the target spike rate of populations of excitatory and inhibitory neurons in the brain is a function of the phase difference between the excitatory and inhibitory spike rates. With this model of homeostatic synaptic plasticity, we are able to simulate the spike rate dynamics seen experimentally by Talathi et al. in a large network of interacting excitatory and inhibitory neurons using two different spiking neuron models. A drift analysis of the spike rates resulting from the homeostatic synaptic plasticity update rule allowed us to determine the type of synapse that may be primarily involved in the spike rate imbalance in the experimental observation by Talathi et al. We find excitatory neurons, particularly those in which the excitatory neuron is presynaptic, have the most influence in producing the diverging spike rates and causing the spike rates to be anti-phase. Our analysis suggests that the excitatory neuronal population, more specifically the excitatory to excitatory synaptic connections, could be implicated in a methodology designed to control epileptic seizures.

  16. Rate-adjusted spike-LFP coherence comparisons from spike-train statistics.

    Science.gov (United States)

    Aoi, Mikio C; Lepage, Kyle Q; Kramer, Mark A; Eden, Uri T

    2015-01-30

    Coherence is a fundamental tool in the analysis of neuronal data and for studying multiscale interactions of single and multiunit spikes with local field potentials. However, when the coherence is used to estimate rhythmic synchrony between spiking and any other time series, the magnitude of the coherence is dependent upon the spike rate. This property is not a statistical bias, but a feature of the coherence function. This dependence confounds cross-condition comparisons of spike-field and spike-spike coherence in electrophysiological experiments. Taking inspiration from correction methods that adjust the spike rate of a recording with bootstrapping ('thinning'), we propose a method of estimating a correction factor for the spike-field and spike-spike coherence that adjusts the coherence to account for this rate dependence. We demonstrate that the proposed rate adjustment is accurate under standard assumptions and derive distributional properties of the estimator. The reduced estimation variance serves to provide a more powerful test of cross-condition differences in spike-LFP coherence than the thinning method and does not require repeated Monte Carlo trials. We also demonstrate some of the negative consequences of failing to account for rate dependence. The proposed spike-field coherence estimator accurately adjusts the spike-field coherence with respect to rate and has well-defined distributional properties that endow the estimator with lower estimation variance than the existing adjustment method.

  17. Memory recall and spike-frequency adaptation

    Science.gov (United States)

    Roach, James P.; Sander, Leonard M.; Zochowski, Michal R.

    2016-05-01

    The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.

  18. Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes

    Directory of Open Access Journals (Sweden)

    Takashi eTakekawa

    2012-03-01

    Full Text Available This study introduces a new spike sorting method that classifies spike waveforms from multiunit recordings into spike trains of individual neurons. In particular, we develop a method to sort a spike mixture generated by a heterogeneous neural population. Such a spike sorting has a significant practical value, but was previously difficult. The method combines a feature extraction method, which we may term multimodality-weighted principal component analysis (mPCA, and a clustering method by variational Bayes for Student’s t mixture model (SVB. The performance of the proposed method was compared with that of other conventional methods for simulated and experimental data sets. We found that the mPCA efficiently extracts highly informative features as clusters clearly separable in a relatively low-dimensional feature space. The SVB was implemented explicitly without relying on Maximum-A-Posterior (MAP inference for the degree of freedom parameters. The explicit SVB is faster than the conventional SVB derived with MAP inference and works more reliably over various data sets that include spiking patterns difficult to sort. For instance, spikes of a single bursting neuron may be separated incorrectly into multiple clusters, whereas those of a sparsely firing neuron tend to be merged into clusters for other neurons. Our method showed significantly improved performance in spike sorting of these difficult neurons. A parallelized implementation of the proposed algorithm (EToS version 3 is available as open-source code at http://etos.sourceforge.net/.

  19. Structured chaos shapes spike-response noise entropy in balanced neural networks.

    Science.gov (United States)

    Lajoie, Guillaume; Thivierge, Jean-Philippe; Shea-Brown, Eric

    2014-01-01

    Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability-spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows-a phenomenon that depends on "extensive chaos," as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.

  20. Structured chaos shapes spike-response noise entropy in balanced neural networks

    Directory of Open Access Journals (Sweden)

    Guillaume eLajoie

    2014-10-01

    Full Text Available Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability -- spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos, as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.

  1. Repetition in English Political Public Speaking

    Institute of Scientific and Technical Information of China (English)

    李红梅

    2010-01-01

    Repetition is frequently used in English political public speaking to make it easy to be remembered and powerful to move the feelings of the public. This paper is intended to analyze the functions of repetition and different levels of repetition to highlight the significance of repetition in English political public speaking and the ability of using it in practice.

  2. Rate-adjusted spike-LFP coherence comparisons from spike-train statistics

    OpenAIRE

    2014-01-01

    Coherence is a fundamental tool in the analysis of neuronal data and for studying multiscale interactions of single and multiunit spikes with local field potentials. However, when the coherence is used to estimate rhythmic synchrony between spiking and any other time series, the magnitude of the coherence is dependent upon the spike rate. This property is not a statistical bias, but a feature of the coherence function. This dependence confounds cross-condition comparisons of spike-field and s...

  3. Spiking neuron network Helmholtz machine.

    Science.gov (United States)

    Sountsov, Pavel; Miller, Paul

    2015-01-01

    An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

  4. Efficient Discovery of Large Synchronous Events in Neural Spike Streams

    CERN Document Server

    Viswanathan, Raajay; Unnikrishnan, K P

    2010-01-01

    We address the problem of finding patterns from multi-neuronal spike trains that give us insights into the multi-neuronal codes used in the brain and help us design better brain computer interfaces. We focus on the synchronous firings of groups of neurons as these have been shown to play a major role in coding and communication. With large electrode arrays, it is now possible to simultaneously record the spiking activity of hundreds of neurons over large periods of time. Recently, techniques have been developed to efficiently count the frequency of synchronous firing patterns. However, when the number of neurons being observed grows they suffer from the combinatorial explosion in the number of possible patterns and do not scale well. In this paper, we present a temporal data mining scheme that overcomes many of these problems. It generates a set of candidate patterns from frequent patterns of smaller size; all possible patterns are not counted. Also we count only a certain well defined subset of occurrences a...

  5. Exact computation of the Maximum Entropy Potential of spiking neural networks models

    CERN Document Server

    Cofre, Rodrigo

    2014-01-01

    Understanding how stimuli and synaptic connectivity in uence the statistics of spike patterns in neural networks is a central question in computational neuroscience. Maximum Entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. But, in spite of good performance in terms of prediction, the ?tting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuro-mimetic models) provide a probabilistic mapping between stimulus, network architecture and spike patterns in terms of conditional proba- bilities. In this paper we build an exact analytical mapping between neuro-mimetic and Maximum Entropy models.

  6. Learning Precise Spike Train-to-Spike Train Transformations in Multilayer Feedforward Neuronal Networks.

    Science.gov (United States)

    Banerjee, Arunava

    2016-05-01

    We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons.

  7. Statistical properties of superimposed stationary spike trains.

    Science.gov (United States)

    Deger, Moritz; Helias, Moritz; Boucsein, Clemens; Rotter, Stefan

    2012-06-01

    The Poisson process is an often employed model for the activity of neuronal populations. It is known, though, that superpositions of realistic, non- Poisson spike trains are not in general Poisson processes, not even for large numbers of superimposed processes. Here we construct superimposed spike trains from intracellular in vivo recordings from rat neocortex neurons and compare their statistics to specific point process models. The constructed superimposed spike trains reveal strong deviations from the Poisson model. We find that superpositions of model spike trains that take the effective refractoriness of the neurons into account yield a much better description. A minimal model of this kind is the Poisson process with dead-time (PPD). For this process, and for superpositions thereof, we obtain analytical expressions for some second-order statistical quantities-like the count variability, inter-spike interval (ISI) variability and ISI correlations-and demonstrate the match with the in vivo data. We conclude that effective refractoriness is the key property that shapes the statistical properties of the superposition spike trains. We present new, efficient algorithms to generate superpositions of PPDs and of gamma processes that can be used to provide more realistic background input in simulations of networks of spiking neurons. Using these generators, we show in simulations that neurons which receive superimposed spike trains as input are highly sensitive for the statistical effects induced by neuronal refractoriness.

  8. What causes a neuron to spike?

    CERN Document Server

    Arcas, B A; Arcas, Blaise Aguera y; Fairhall, Adrienne

    2003-01-01

    The computation performed by a neuron can be formulated as a combination of dimensional reduction in stimulus space and the nonlinearity inherent in a spiking output. White noise stimulus and reverse correlation (the spike-triggered average and spike-triggered covariance) are often used in experimental neuroscience to `ask' neurons which dimensions in stimulus space they are sensitive to, and to characterize the nonlinearity of the response. In this paper, we apply reverse correlation to the simplest model neuron with temporal dynamics--the leaky integrate-and-fire model--and find that even for this simple case standard techniques do not recover the known neural computation. To overcome this, we develop novel reverse correlation techniques by selectively analyzing only `isolated' spikes, and taking explicit account of the extended silences that precede these isolated spikes. We discuss the implications of our methods to the characterization of neural adaptation. Although these methods are developed in the con...

  9. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.

    Directory of Open Access Journals (Sweden)

    Riccardo Storchi

    Full Text Available Local Field Potentials (LFPs integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding and in the clinics (e.g. for improving invasive Brain-Machine Interface devices. However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

  11. REPETITIVE CLUSTER-TILTED ALGEBRAS

    Institute of Scientific and Technical Information of China (English)

    Zhang Shunhua; Zhang Yuehui

    2012-01-01

    Let H be a finite-dimensional hereditary algebra over an algebraically closed field k and CFm be the repetitive cluster category of H with m ≥ 1.We investigate the properties of cluster tilting objects in CFm and the structure of repetitive clustertilted algebras.Moreover,we generalize Theorem 4.2 in [12](Buan A,Marsh R,Reiten I.Cluster-tilted algebra,Trans.Amer.Math.Soc.,359(1)(2007),323-332.) to the situation of CFm,and prove that the tilting graph KCFm of CFm is connected.

  12. Processing of species-specific auditory patterns in the cricket brain by ascending, local, and descending neurons during standing and walking.

    Science.gov (United States)

    Zorović, M; Hedwig, B

    2011-05-01

    The recognition of the male calling song is essential for phonotaxis in female crickets. We investigated the responses toward different models of song patterns by ascending, local, and descending neurons in the brain of standing and walking crickets. We describe results for two ascending, three local, and two descending interneurons. Characteristic dendritic and axonal arborizations of the local and descending neurons indicate a flow of auditory information from the ascending interneurons toward the lateral accessory lobes and point toward the relevance of this brain region for cricket phonotaxis. Two aspects of auditory processing were studied: the tuning of interneuron activity to pulse repetition rate and the precision of pattern copying. Whereas ascending neurons exhibited weak, low-pass properties, local neurons showed both low- and band-pass properties, and descending neurons represented clear band-pass filters. Accurate copying of single pulses was found at all three levels of the auditory pathway. Animals were walking on a trackball, which allowed an assessment of the effect that walking has on auditory processing. During walking, all neurons were additionally activated, and in most neurons, the spike rate was correlated to walking velocity. The number of spikes elicited by a chirp increased with walking only in ascending neurons, whereas the peak instantaneous spike rate of the auditory responses increased on all levels of the processing pathway. Extra spiking activity resulted in a somewhat degraded copying of the pulse pattern in most neurons.

  13. Spiking neural P systems with anti-spikes and without annihilating priority as number acceptors

    Institute of Scientific and Technical Information of China (English)

    Gangjun Tan,Tao Song,; Zhihua Chen

    2014-01-01

    Spiking neural P systems with anti-spikes (ASN P sys-tems) are variant forms of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, spikes and anti-spikes wil immediately an-nihilate each other in a maximal way. In this paper, a restricted variant of ASN P systems, cal ed ASN P systems without anni-hilating priority, is considered, where the annihilating rule is used as the standard rule, i.e., it is not obligatory to use in the neuron associated with both spikes and anti-spikes. If the annihilating rule is used in a neuron, the annihilation wil consume one time unit. As a result, such systems using two categories of spiking rules (iden-tified by (a, a) and (a, ¯a)) can achieve Turing completeness as number accepting devices.

  14. Recurrent Spiking Networks Solve Planning Tasks

    Science.gov (United States)

    Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

    2016-02-01

    A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate injected input this dynamics is shaped to generate high-reward state trajectories. A general class of reward-modulated plasticity rules for these afferent synapses is presented. The updates optimize the likelihood of getting a reward through a variant of an Expectation Maximization algorithm and learning is guaranteed to convergence to a local maximum. We find that the network dynamics are qualitatively similar to transient firing patterns during planning and foraging in the hippocampus of awake behaving rats. The model extends classical attractor models and provides a testable prediction on identifying modulating contextual information. In a real robot arm reaching and obstacle avoidance task the ability to represent multiple task solutions is investigated. The neural planning method with its local update rules provides the basis for future neuromorphic hardware implementations with promising potentials like large data processing abilities and early initiation of strategies to avoid dangerous situations in robot co-worker scenarios.

  15. Asynchronous Rate Chaos in Spiking Neuronal Circuits

    Science.gov (United States)

    Harish, Omri; Hansel, David

    2015-01-01

    The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results. PMID:26230679

  16. Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.

    Science.gov (United States)

    Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

    2016-01-01

    Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.

  17. Automated analysis of calcium spiking profiles with CaSA software: two case studies from root-microbe symbioses.

    Science.gov (United States)

    Russo, Giulia; Spinella, Salvatore; Sciacca, Eva; Bonfante, Paola; Genre, Andrea

    2013-12-26

    Repeated oscillations in intracellular calcium (Ca2+) concentration, known as Ca2+ spiking signals, have been described in plants for a limited number of cellular responses to biotic or abiotic stimuli and most notably the common symbiotic signaling pathway (CSSP) which mediates the recognition by their plant hosts of two endosymbiotic microbes, arbuscular mycorrhizal (AM) fungi and nitrogen fixing rhizobia. The detailed analysis of the complexity and variability of the Ca2+ spiking patterns which have been revealed in recent studies requires both extensive datasets and sophisticated statistical tools. As a contribution, we have developed automated Ca2+ spiking analysis (CaSA) software that performs i) automated peak detection, ii) statistical analyses based on the detected peaks, iii) autocorrelation analysis of peak-to-peak intervals to highlight major traits in the spiking pattern.We have evaluated CaSA in two experimental studies. In the first, CaSA highlighted unpredicted differences in the spiking patterns induced in Medicago truncatula root epidermal cells by exudates of the AM fungus Gigaspora margarita as a function of the phosphate concentration in the growth medium of both host and fungus. In the second study we compared the spiking patterns triggered by either AM fungal or rhizobial symbiotic signals. CaSA revealed the existence of different patterns in signal periodicity, which are thought to contribute to the so-called Ca2+ signature. We therefore propose CaSA as a useful tool for characterizing oscillatory biological phenomena such as Ca2+ spiking.

  18. Repetitive elements in parasitic protozoa

    Directory of Open Access Journals (Sweden)

    Clayton Christine

    2010-05-01

    Full Text Available Abstract A recent paper published in BMC Genomics suggests that retrotransposition may be active in the human gut parasite Entamoeba histolytica. This adds to our knowledge of the various types of repetitive elements in parasitic protists and the potential influence of such elements on pathogenicity. See research article http://www.biomedcentral.com/1471-2164/11/321

  19. Bursts and isolated spikes code for opposite movement directions in midbrain electrosensory neurons.

    Directory of Open Access Journals (Sweden)

    Navid Khosravi-Hashemi

    Full Text Available Directional selectivity, in which neurons respond strongly to an object moving in a given direction but weakly or not at all to the same object moving in the opposite direction, is a crucial computation that is thought to provide a neural correlate of motion perception. However, directional selectivity has been traditionally quantified by using the full spike train, which does not take into account particular action potential patterns. We investigated how different action potential patterns, namely bursts (i.e. packets of action potentials followed by quiescence and isolated spikes, contribute to movement direction coding in a mathematical model of midbrain electrosensory neurons. We found that bursts and isolated spikes could be selectively elicited when the same object moved in opposite directions. In particular, it was possible to find parameter values for which our model neuron did not display directional selectivity when the full spike train was considered but displayed strong directional selectivity when bursts or isolated spikes were instead considered. Further analysis of our model revealed that an intrinsic burst mechanism based on subthreshold T-type calcium channels was not required to observe parameter regimes for which bursts and isolated spikes code for opposite movement directions. However, this burst mechanism enhanced the range of parameter values for which such regimes were observed. Experimental recordings from midbrain neurons confirmed our modeling prediction that bursts and isolated spikes can indeed code for opposite movement directions. Finally, we quantified the performance of a plausible neural circuit and found that it could respond more or less selectively to isolated spikes for a wide range of parameter values when compared with an interspike interval threshold. Our results thus show for the first time that different action potential patterns can differentially encode movement and that traditional measures of

  20. Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

    Science.gov (United States)

    Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

    2016-12-01

    Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

  1. Transfer of Timing Information from RGC to LGN Spike Trains

    Science.gov (United States)

    Teich, Malvin C.; Lowen, Steven B.; Saleh, Bahaa E. A.; Kaplan, Ehud

    1998-03-01

    We have studied the firing patterns of retinal ganglion cells (RGCs) and their target lateral geniculate nucleus (LGN) cells. We find that clusters of spikes in the RGC neural firing pattern appear at the LGN output essentially unchanged, while isolated RGC firing events are more likely to be eliminated; thus the LGN action-potential sequence is therefore not merely a randomly deleted version of the RGC spike train. Employing information-theoretic techniques we developed for point processes,(B. E. A. Saleh and M. C. Teich, Phys. Rev. Lett.) 58, 2656--2659 (1987). we are able to estimate the information efficiency of the LGN neuronal output --- the proportion of the variation in the LGN firing pattern that carries information about its associated RGC input. A suitably modified integrate-and-fire neural model reproduces both the enhanced clustering in the LGN data (which accounts for the increased coefficient of variation) and the measured value of information efficiency, as well as mimicking the results of other observed statistical measures. Reliable information transmission therefore coexists with fractal fluctuations, which appear in RGC and LGN firing patterns.(M. C. Teich, C. Heneghan, S. B. Lowen, T. Ozaki, and E. Kaplan, J. Opt. Soc. Am. A) 14, 529--546 (1997).

  2. Repetition suppression and repetition priming are processing outcomes.

    Science.gov (United States)

    Wig, Gagan S

    2012-01-01

    Abstract There is considerable evidence that repetition suppression (RS) is a cortical signature of previous exposure to the environment. In many instances RS in specific brain regions is accompanied by improvements in specific behavioral measures; both observations are outcomes of repeated processing. In understanding the mechanism by which brain changes give rise to behavioral changes, it is important to consider what aspect of the environment a given brain area or set of areas processes, and how this might be expressed behaviorally.

  3. Homoclinic Spike adding in a neuronal model in the presence of noise

    Science.gov (United States)

    Fuwape, Ibiyinka; Neiman, Alexander; Shilnikov, Andrey

    2008-03-01

    We study the influence of noise on a spike adding transitions within the bursting activity in a Hodgkin-Huxley-type model of the leech heart interneuron. Spike adding in this model occur via homoclinic bifurcation of a saddle periodic orbit. Although narrow chaotic regions are observed near bifurcation transition, overall bursting dynamics is regular and is characterized by a constant number of spikes per burst. Experimental studies, however, show variability of bursting patterns whereby number of spikes per burst varies randomly. Thus, introduction of external synaptic noise is a necessary step to account for variability of burst durations observed experimentally. We show that near every such transition the neuron is highly sensitive to random perturbations that lead to and enhance broadly the regions of chaotic dynamics of the cell. For each spike adding transition there is a critical noise level beyond which the dynamics of the neuron becomes chaotic throughout the entire region of the given transition. Noise-induced chaotic dynamics is characterized in terms of the Lyapunov exponents and the Shannon entropy and reflects variability of firing patterns with various numbers of spikes per burst, traversing wide range of the neuron's parameters

  4. Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

    Directory of Open Access Journals (Sweden)

    Michael Krumin

    2010-01-01

    Full Text Available Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden’’ Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method.

  5. Non-parametric method for separating domestic hot water heating spikes and space heating

    DEFF Research Database (Denmark)

    Bacher, Peder; de Saint-Aubain, Philip Anton; Christiansen, Lasse Engbo;

    2016-01-01

    In this paper a method for separating spikes from a noisy data series, where the data change and evolve over time, is presented. The method is applied on measurements of the total heat load for a single family house. It relies on the fact that the domestic hot water heating is a process generating...... short-lived spikes in the time series, while the space heating changes in slower patterns during the day dependent on the climate and user behavior. The challenge is to separate the domestic hot water heating spikes from the space heating without affecting the natural noise in the space heating...... measurements. The assumption behind the developed method is that the space heating can be estimated by a non-parametric kernel smoother, such that every value significantly above this kernel smoother estimate is identified as a domestic hot water heating spike. First, it is showed how a basic kernel smoothing...

  6. Cohesive Function of Lexical Repetition in Text

    Institute of Scientific and Technical Information of China (English)

    张莉; 卢沛沛

    2013-01-01

    Lexical repetition is the most direct form of lexical cohesion,which is the central device for making texts hang together. Although repetition is the most direct way to emphasize,it performs the cohesive effect more apparently.

  7. Cortico-striatal spike-timing dependent plasticity after activation of subcortical pathways

    Directory of Open Access Journals (Sweden)

    Jan M Schulz

    2010-07-01

    Full Text Available Cortico-striatal spike-timing dependent plasticity (STDP is modulated by dopamine in vitro. The present study investigated STDP in vivo using alternative procedures for modulating dopaminergic inputs. Postsynaptic potentials (PSP were evoked in intracellularly recorded spiny neurons by electrical stimulation of the contralateral motor cortex. PSPs often consisted of up to three distinct components, likely representing distinct cortico-striatal pathways. After baseline recording, bicuculline (BIC was ejected into the superior colliculus (SC to disinhibit visual pathways to the dopamine cells and striatum. Repetitive cortical stimulation (~60; 0.2 Hz was then paired with postsynaptic spike discharge induced by an intracellular current pulse, with each pairing followed 250 ms later by a light flash to the contralateral eye (n=13. Changes in PSPs, measured as the maximal slope normalised to 5 min pre, ranged from potentiation (~120% to depression (~80%. The determining factor was the relative timing between PSP components and spike: PSP components coinciding or closely following the spike tended towards potentiation, whereas PSP components preceding the spike were depressed. Importantly, STDP was only seen in experiments with successful BIC-mediated disinhibition (n=10. Cortico-striatal high-frequency stimulation (50 pulses at 100 Hz followed 100 ms later by a light flash did not induce more robust synaptic plasticity (n=9. However, an elevated post-light spike rate correlated with depression across plasticity protocols (R2=0.55, p=0.009, n=11 active neurons. These results confirm that the direction of cortico-striatal plasticity is determined by the timing of pre- and postsynaptic activity and that synaptic modification is dependent on the activation of additional subcortical inputs.

  8. Fitting Neuron Models to Spike Trains

    Science.gov (United States)

    Rossant, Cyrille; Goodman, Dan F. M.; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K.; Brette, Romain

    2011-01-01

    Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925

  9. Frequency of Rolandic Spikes in ADHD

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2003-10-01

    Full Text Available The frequency of rolandic spikes in nonepileptic children with attention deficit hyperactivity disorder (ADHD was compared with a control group of normal school-aged children in a study at the University of Frankfurt, Germany.

  10. Noise-induced interspike interval correlations and spike train regularization in spike-triggered adapting neurons

    CERN Document Server

    Urdapilleta, Eugenio

    2016-01-01

    Spike generation in neurons produces a temporal point process, whose statistics is governed by intrinsic phenomena and the external incoming inputs to be coded. In particular, spike-evoked adaptation currents support a slow temporal process that conditions spiking probability at the present time according to past activity. In this work, we study the statistics of interspike interval correlations arising in such non-renewal spike trains, for a neuron model that reproduces different spike modes in a small adaptation scenario. We found that correlations are stronger as the neuron fires at a particular firing rate, which is defined by the adaptation process. When set in a subthreshold regime, the neuron may sustain this particular firing rate, and thus induce correlations, by noise. Given that, in this regime, interspike intervals are negatively correlated at any lag, this effect surprisingly implies a reduction in the variability of the spike count statistics at a finite noise intensity.

  11. Noise-induced interspike interval correlations and spike train regularization in spike-triggered adapting neurons

    Science.gov (United States)

    Urdapilleta, Eugenio

    2016-09-01

    Spike generation in neurons produces a temporal point process, whose statistics is governed by intrinsic phenomena and the external incoming inputs to be coded. In particular, spike-evoked adaptation currents support a slow temporal process that conditions spiking probability at the present time according to past activity. In this work, we study the statistics of interspike interval correlations arising in such non-renewal spike trains, for a neuron model that reproduces different spike modes in a small adaptation scenario. We found that correlations are stronger as the neuron fires at a particular firing rate, which is defined by the adaptation process. When set in a subthreshold regime, the neuron may sustain this particular firing rate, and thus induce correlations, by noise. Given that, in this regime, interspike intervals are negatively correlated at any lag, this effect surprisingly implies a reduction in the variability of the spike count statistics at a finite noise intensity.

  12. Object color affects identification and repetition priming.

    Science.gov (United States)

    Uttl, Bob; Graf, Peter; Santacruz, Pilar

    2006-10-01

    We investigated the influence of color on the identification of both non-studied and studied objects. Participants studied black and white and color photos of common objects and memory was assessed with an identification test. Consistent with our meta-analysis of prior research, we found that objects were easier to identify from color than from black and white photos. We also found substantial priming in all conditions, and study-to-test changes in an object's color reduced the magnitude of priming. Color-specific priming effects were large for color-complex objects, but minimal for color-simple objects. The pattern and magnitude of priming effects was not influenced either by the extent to which an object always appears in the same color (i.e., whether a color is symptomatic of an object) or by the object's origin (natural versus fabricated). We discuss the implications of our findings for theoretical accounts of object perception and repetition priming.

  13. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  14. Training Deep Spiking Neural Networks using Backpropagation

    Directory of Open Access Journals (Sweden)

    Jun Haeng Lee

    2016-11-01

    Full Text Available Deep spiking neural networks (SNNs hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  15. Recurrent symptomatic intraocular pressure spikes during hemodialysis in a patient with unilateral anterior uveitis

    Directory of Open Access Journals (Sweden)

    Lim Su-Ho

    2013-02-01

    Full Text Available Abstract Background The relationship between intraocular pressure (IOP changes and hemodialysis has been evaluated for several decades. However, no report on an IOP rise in uveitis patients during hemodialysis has been previously documented. This report describes the case of an uveitis patient with repetitive IOP spikes associated with severe ocular pain during hemodialysis sessions, which resolved after glaucoma filtering surgery. Case presentation A 47-year-old male with diabetes and hypertension had complained of recurrent ocular pain in the left eye during hemodialysis sessions. A slit-lamp examination showed diffuse corneal epithelial edema with several white keratic precipitates and inflammatory cells (Grade 3+ in the anterior chamber of the left eye. No visible neovascularization or synechiae were visible on the iris or angle. Topical glaucoma eye-drops and intravenous mannitol before hemodialysis did not prevent subsequent painful IOP spikes in the left eye. At the end of hemodialysis, IOP averaged ~40 mmHg. After trabeculectomy with mitomycin C in the left eye, his IOP stabilized in the low-teens (range, 10–14 mmHg and no painful IOP spikes occurred during hemodialysis over the first postoperative year. Conclusion We present a case of recurrent painful IOP spikes during hemodialysis in a patient with unilateral anterior uveitis unresponsive to conventional medical treatment prior to hemodialysis. To our knowledge, this is the first case report of repetitive symptomatic IOP rise during hemodialysis in an uveitic glaucoma patient. This case highlights the importance of the awareness of the possibility that IOP may rise intolerably during hemodialysis in uveitis patients with a compromised outflow facility.

  16. Investigating repetition and change in musical rhythm by functional MRI.

    Science.gov (United States)

    Danielsen, A; Otnæss, M K; Jensen, J; Williams, S C R; Ostberg, B C

    2014-09-05

    Groove-based rhythm is a basic and much appreciated feature of Western popular music. It is commonly associated with dance, movement and pleasure and is characterized by the repetition of a basic rhythmic pattern. At various points in the musical course, drum breaks occur, representing a change compared to the repeated pattern of the groove. In the present experiment, we investigated the brain response to such drum breaks in a repetitive groove. Participants were scanned with functional magnetic resonance imaging (fMRI) while listening to a previously unheard naturalistic groove with drum breaks at uneven intervals. The rhythmic pattern and the timing of its different parts as performed were the only aspects that changed from the repetitive sections to the breaks. Differences in blood oxygen level-dependent activation were analyzed. In contrast to the repetitive parts, the drum breaks activated the left cerebellum, the right inferior frontal gyrus (RIFG), and the superior temporal gyri (STG) bilaterally. A tapping test using the same stimulus showed an increase in the standard deviation of inter-tap-intervals in the breaks versus the repetitive parts, indicating extra challenges for auditory-motor integration in the drum breaks. Both the RIFG and STG have been associated with structural irregularity and increase in musical-syntactical complexity in several earlier studies, whereas the left cerebellum is known to play a part in timing. Together these areas may be recruited in the breaks due to a prediction error process whereby the internal model is being updated. This concurs with previous research suggesting a network for predictive feed-forward control that comprises the cerebellum and the cortical areas that were activated in the breaks.

  17. Kv3.3 channels at the Purkinje cell soma are necessary for generation of the classical complex spike waveform.

    Science.gov (United States)

    Zagha, Edward; Lang, Eric J; Rudy, Bernardo

    2008-02-06

    Voltage-gated potassium channel subunit Kv3.3 is prominently expressed in cerebellar Purkinje cells and is known to be important for cerebellar function, as human and mouse movement disorders result from mutations in Kv3.3. To understand these behavioral deficits, it is necessary to know the role of Kv3.3 channels on the physiological responses of Purkinje cells. We studied the function of Kv3.3 channels in regulating the synaptically evoked Purkinje cell complex spike, the massive postsynaptic response to the activation of climbing fiber afferents, believed to be fundamental to cerebellar physiology. Acute slice recordings revealed that Kv3.3 channels are required for generation of the repetitive spikelets of the complex spike. We found that spikelet expression is regulated by somatic, and not by dendritic, Kv3 activity, which is consistent with dual somatic-dendritic recordings that demonstrate spikelet generation at axosomatic membranes. Simulations of Purkinje cell Na+ currents show that the unique electrical properties of Kv3 and resurgent Na+ channels are coordinated to limit accumulation of Na+ channel inactivation and enable rapid, repetitive firing. We additionally show that Kv3.3 knock-out mice produce altered complex spikes in vitro and in vivo, which is likely a cellular substrate of the cerebellar phenotypes observed in these mice. This characterization presents new tools to study complex spike function, cerebellar signaling, and Kv3.3-dependent human and mouse phenotypes.

  18. Spike Detection Based on Normalized Correlation with Automatic Template Generation

    Directory of Open Access Journals (Sweden)

    Wen-Jyi Hwang

    2014-06-01

    Full Text Available A novel feedback-based spike detection algorithm for noisy spike trains is presented in this paper. It uses the information extracted from the results of spike classification for the enhancement of spike detection. The algorithm performs template matching for spike detection by a normalized correlator. The detected spikes are then sorted by the OSortalgorithm. The mean of spikes of each cluster produced by the OSort algorithm is used as the template of the normalized correlator for subsequent detection. The automatic generation and updating of templates enhance the robustness of the spike detection to input trains with various spike waveforms and noise levels. Experimental results show that the proposed algorithm operating in conjunction with OSort is an efficient design for attaining high detection and classification accuracy for spike sorting.

  19. Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

    Science.gov (United States)

    Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

    2016-01-01

    In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision. PMID:27857680

  20. Multi-scale detection of rate changes in spike trains with weak dependencies.

    Science.gov (United States)

    Messer, Michael; Costa, Kauê M; Roeper, Jochen; Schneider, Gaby

    2017-04-01

    The statistical analysis of neuronal spike trains by models of point processes often relies on the assumption of constant process parameters. However, it is a well-known problem that the parameters of empirical spike trains can be highly variable, such as for example the firing rate. In order to test the null hypothesis of a constant rate and to estimate the change points, a Multiple Filter Test (MFT) and a corresponding algorithm (MFA) have been proposed that can be applied under the assumption of independent inter spike intervals (ISIs). As empirical spike trains often show weak dependencies in the correlation structure of ISIs, we extend the MFT here to point processes associated with short range dependencies. By specifically estimating serial dependencies in the test statistic, we show that the new MFT can be applied to a variety of empirical firing patterns, including positive and negative serial correlations as well as tonic and bursty firing. The new MFT is applied to a data set of empirical spike trains with serial correlations, and simulations show improved performance against methods that assume independence. In case of positive correlations, our new MFT is necessary to reduce the number of false positives, which can be highly enhanced when falsely assuming independence. For the frequent case of negative correlations, the new MFT shows an improved detection probability of change points and thus, also a higher potential of signal extraction from noisy spike trains.

  1. Roller Coaster Scanning reveals spontaneous triggering of dendritic spikes in CA1 interneurons.

    Science.gov (United States)

    Katona, Gergely; Kaszás, Attila; Turi, Gergely F; Hájos, Norbert; Tamás, Gábor; Vizi, E Sylvester; Rózsa, Balázs

    2011-02-01

    Inhibitory interneurons are considered to be the controlling units of neural networks, despite their sparse number and unique morphological characteristics compared with excitatory pyramidal cells. Although pyramidal cell dendrites have been shown to display local regenerative events--dendritic spikes (dSpikes)--evoked by artificially patterned stimulation of synaptic inputs, no such studies exist for interneurons or for spontaneous events. In addition, imaging techniques have yet to attain the required spatial and temporal resolution for the detection of spontaneously occurring events that trigger dSpikes. Here we describe a high-resolution 3D two-photon laser scanning method (Roller Coaster Scanning) capable of imaging long dendritic segments resolving individual spines and inputs with a temporal resolution of a few milliseconds. By using this technique, we found that local, NMDA receptor-dependent dSpikes can be observed in hippocampal CA1 stratum radiatum interneurons during spontaneous network activities in vitro. These NMDA spikes appear when approximately 10 spatially clustered inputs arrive synchronously and trigger supralinear integration in dynamic interaction zones. In contrast to the one-to-one relationship between computational subunits and dendritic branches described in pyramidal cells, here we show that interneurons have relatively small (∼14 μm) sliding interaction zones. Our data suggest a unique principle as to how interneurons integrate synaptic information by local dSpikes.

  2. The introduction of dengue vaccine may temporarily cause large spikes in prevalence.

    Science.gov (United States)

    Pandey, A; Medlock, J

    2015-04-01

    A dengue vaccine is expected to be available within a few years. Once vaccine is available, policy-makers will need to develop suitable policies to allocate the vaccine. Mathematical models of dengue transmission predict complex temporal patterns in prevalence, driven by seasonal oscillations in mosquito abundance. In particular, vaccine introduction may induce a transient period immediately after vaccine introduction where prevalence can spike higher than in the pre-vaccination period. These spikes in prevalence could lead to doubts about the vaccination programme among the public and even among decision-makers, possibly impeding the vaccination programme. Using simple dengue transmission models, we found that large transient spikes in prevalence are robust phenomena that occur when vaccine coverage and vaccine efficacy are not either both very high or both very low. Despite the presence of transient spikes in prevalence, the models predict that vaccination does always reduce the total number of infections in the 15 years after vaccine introduction. We conclude that policy-makers should prepare for spikes in prevalence after vaccine introduction to mitigate the burden of these spikes and to accurately measure the effectiveness of the vaccine programme.

  3. Wavelet analysis of solar decimeter spikes

    Science.gov (United States)

    Fernandes, F. C. R.; Bolzan, M. J. A.; Rosa, R. R.; Prestes, A.; Cecatto, J. R.; Sawant, H. S.

    2012-04-01

    The Brazilian Solar Spectroscope (BSS), in operation at INPE, Brazil, have recorded a group of radio spikes observed on June 24, 1999 (16:53 - 16: 56 UT), in the decimeter frequency range of 1000-2500 MHz, with high temporal resolution of 100 ms and spectral resolution of 10 MHz. This event presents distinct clusters of spikes with intermittent behavior. The spectral and temporal behaviors of the observed groups of spikes of radio were investigated, applying wavelet techniques, which permits to determine the cadence of the clusters. From the dynamic spectra, the following observational parameters were determined: the total duration of the event and of each cluster, the total frequency band and cluster bands, in the case of the harmonic structures. The wavelet analysis shows an average periodicity of about 17 seconds (and harmonics) for the cadence of intermittent clusters of spikes. The analysis also suggested a semi-harmonic frequency ratio of 1:1.2. The same methodology of analysis is being applied to other selected groups of spikes recorded by BSS. These results will be presented and discussed.

  4. Spike Code Flow in Cultured Neuronal Networks.

    Science.gov (United States)

    Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei

    2016-01-01

    We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

  5. Circuit considerations for repetitive railguns

    Energy Technology Data Exchange (ETDEWEB)

    Honih, E.M.

    1986-01-01

    Railgun electromagnetic launchers have significant military and scientific potential. They provide direct conversion of electrical energy to projectile kinetic energy, and they offer the hope of achieving projectile velocities greatly exceeding the limits of conventional guns. With over 10 km/sec already demonstrated, railguns are attracting attention for tactical and strategic weapons systems and for scientific equation-of-state research. The full utilization of railguns will require significant improvements in every aspect of system design - projectile, barrel, and power source - to achieve operation on a large scale. This paper will review fundamental aspects of railguns, with emphasis on circuit considerations and repetitive operation.

  6. Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model.

    Directory of Open Access Journals (Sweden)

    Wondimu Teka

    2014-03-01

    Full Text Available The voltage trace of neuronal activities can follow multiple timescale dynamics that arise from correlated membrane conductances. Such processes can result in power-law behavior in which the membrane voltage cannot be characterized with a single time constant. The emergent effect of these membrane correlations is a non-Markovian process that can be modeled with a fractional derivative. A fractional derivative is a non-local process in which the value of the variable is determined by integrating a temporal weighted voltage trace, also called the memory trace. Here we developed and analyzed a fractional leaky integrate-and-fire model in which the exponent of the fractional derivative can vary from 0 to 1, with 1 representing the normal derivative. As the exponent of the fractional derivative decreases, the weights of the voltage trace increase. Thus, the value of the voltage is increasingly correlated with the trajectory of the voltage in the past. By varying only the fractional exponent, our model can reproduce upward and downward spike adaptations found experimentally in neocortical pyramidal cells and tectal neurons in vitro. The model also produces spikes with longer first-spike latency and high inter-spike variability with power-law distribution. We further analyze spike adaptation and the responses to noisy and oscillatory input. The fractional model generates reliable spike patterns in response to noisy input. Overall, the spiking activity of the fractional leaky integrate-and-fire model deviates from the spiking activity of the Markovian model and reflects the temporal accumulated intrinsic membrane dynamics that affect the response of the neuron to external stimulation.

  7. Spike timing and synaptic dynamics at the awake thalamocortical synapse.

    Science.gov (United States)

    Swadlow, Harvey A; Bezdudnaya, Tatiana; Gusev, Alexander G

    2005-01-01

    Thalamocortical (TC) neurons form only a small percentage of the synapses onto neurons of cortical layer 4, but the response properties of these cortical neurons are arguably dominated by thalamic input. This discrepancy is explained, in part, by studies showing that TC synapses are of high efficacy. However, TC synapses display activity-dependent depression. Because of this, in vitro measures of synaptic efficacy will not reflect the situation in vivo, where different neuronal populations have widely varying levels of "spontaneous" activity. Indeed, TC neurons of awake subjects generate high rates of spontaneous activity that would be expected, in a depressing synapse, to result in a chronic state of synaptic depression. Here, we review recent work in the somatosensory thalamocortical system of awake rabbits in which the relationship between TC spike timing and TC synaptic efficacy was examined during both thalamic "relay mode" (alert state) and "burst mode" (drowsy state). Two largely independent methodological approaches were used. First, we employed cross-correlation methods to examine the synaptic impact of single TC "barreloid" neurons on a single neuronal subtype in the topographically aligned layer 4 "barrel" - putative fast-spike inhibitory interneurons. We found that the initial spike of a TC burst, as well as isolated TC spikes with long preceding interspike intervals (ISIs) elicited postsynaptic action potentials far more effectively than did TC impulses with short ISIs. Our second approach took a broader view of the postsynaptic impact of TC impulses. In these experiments we examined spike-triggered extracellular field potentials and synaptic currents (using current source-density analysis) generated through the depths of a cortical barrel column by the impulses of single topographically aligned TC neurons. We found that (a) closely neighboring TC neurons may elicit very different patterns of monosynaptic activation within layers 4 and 6 of the aligned

  8. Longitudinally excited CO2 laser with short laser pulse operating at high repetition rate

    Science.gov (United States)

    Li, Jianhui; Uno, Kazuyuki; Akitsu, Tetsuya; Jitsuno, Takahisa

    2016-11-01

    A short-pulse longitudinally excited CO2 laser operating at a high repetition rate was developed. The discharge tube was made of a 45 cm-long or 60 cm-long dielectric tube with an inner diameter of 16 mm and two metallic electrodes at the ends of the tube. The optical cavity was formed by a ZnSe output coupler with a reflectivity of 85% and a high-reflection mirror. Mixed gas (CO2:N2:He = 1:1:2) was flowed into the discharge tube. A high voltage of about 33 kV with a rise time of about 200 ns was applied to the discharge tube. At a repetition rate of 300 Hz and a gas pressure of 3.4 kPa, the 45 cm-long discharge tube produced a short laser pulse with a laser pulse energy of 17.5 mJ, a spike pulse energy of 0.2 mJ, a spike width of 153 ns, and a pulse tail length of 90 μs. The output power was 5.3 W. The laser pulse waveform did not depend on the repetition rate, but the laser beam profile did. At a low repetition rate of less than 50 Hz, the laser beam had a doughnut-like shape. However, at a high repetition rate of more than 150 Hz, the discharge concentrated at the center of the discharge tube, and the intensity at the center of the laser beam was higher. The laser beam profile depended on the distribution of the discharge. An output power of 7.0 W was achieved by using the 60 cm-long tube.

  9. Spiking models for level-invariant encoding

    Directory of Open Access Journals (Sweden)

    Romain eBrette

    2012-01-01

    Full Text Available Levels of ecological sounds vary over several orders of magnitude,but the firing rate and membrane potential of a neuron are much more limited in range.In binaural neurons of the barn owl, tuning to interaural delays is independent oflevel differences. Yet a monaural neuron with a fixed threshold should fire earlier in responseto louder sounds, which would disrupt the tuning of these neurons. %, resulting in shifts in delay tuning for interaural level differences.How could spike timing be independent of input level?Here I derive theoretical conditions for a spiking model tobe insensitive to input level.The key property is a dynamic change in spike threshold.I then show how level invariance can be physiologically implemented,with specific ionic channel properties.It appears that these ingredients are indeed present inmonaural neurons of the sound localization pathway of birds and mammals.

  10. Filter based phase distortions in extracellular spikes.

    Science.gov (United States)

    Yael, Dorin; Bar-Gad, Izhar

    2017-01-01

    Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results.

  11. Filter based phase distortions in extracellular spikes

    Science.gov (United States)

    Yael, Dorin

    2017-01-01

    Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results. PMID:28358895

  12. Evoking prescribed spike times in stochastic neurons

    Science.gov (United States)

    Doose, Jens; Lindner, Benjamin

    2017-09-01

    Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.

  13. High-threshold, Kv3-like potassium currents in magnocellular neurosecretory neurons and their role in spike repolarization.

    Science.gov (United States)

    Shevchenko, Talent; Teruyama, Ryoichi; Armstrong, William E

    2004-11-01

    We identified Kv3-like high-threshold K+ currents in hypothalamic supraoptic neurons using whole cell recordings in hypothalamic slices and in acutely dissociated neurons. Tetraethylammonium (TEA)-sensitive currents (Kv3-like channels. In slices, tests with 0.01-0.7 mM TEA produced an IC50 of 200-300 nM for both fast and persistent currents. The fast transient current was similar to currents associated with the expression of Kv3.4 subunits, given that it was sensitive to BDS-I (100 nM). The persistent TEA-sensitive current appeared similar to those attributed to Kv3.1/3.2 subunits. Although qualitatively similar, oxytocin (OT) and vasopressin (VP) neurons in slices differed in the stronger presence of persistent current in VP neurons. In both cell types, the IC50 for TEA-induced spike broadening was similar to that observed for current suppression in voltage clamp. However, TEA had a greater effect on the spike width of VP neurons than of OT neurons. Immunochemical studies revealed a stronger expression of the Kv3.1b alpha-subunit in VP neurons, which may be related to the greater importance of this current type in VP spike repolarization. Because OT and VP neurons are not considered fast firing, but do exhibit frequency- and calcium-dependent spike broadening, Kv3-like currents may be important for maintaining spike width and calcium influx within acceptable limits during repetitive firing.

  14. Time-frequency analysis of spike-wave discharges using a modified wavelet transform

    NARCIS (Netherlands)

    Bosnyakova, D.Y.; Gabova, A.; Kuznetsova, G.D.; Obukhov, Y.; Midzyanovskaya, I.S.; Salonin, D.V.; Rijn, C.M. van; Coenen, A.M.L.; Tuomisto, L.; Luijtelaar, E.L.J.M. van

    2006-01-01

    The continuous Morlet wavelet transform was used for the analysis of the time-frequency pattern of spike-wave discharges (SWD) as can be recorded in a genetic animal model of absence epilepsy (rats of the WAG/Rij strain). We developed a new wavelet transform that allows to obtain the time-frequency

  15. Generation of spatiotemporally correlated spike trains and local field potentials using a multivariate autoregressive process.

    Science.gov (United States)

    Gutnisky, Diego A; Josić, Kresimir

    2010-05-01

    Experimental advances allowing for the simultaneous recording of activity at multiple sites have significantly increased our understanding of the spatiotemporal patterns in neural activity. The impact of such patterns on neural coding is a fundamental question in neuroscience. The simulation of spike trains with predetermined activity patterns is therefore an important ingredient in the study of potential neural codes. Such artificially generated spike trains could also be used to manipulate cortical neurons in vitro and in vivo. Here, we propose a method to generate spike trains with given mean firing rates and cross-correlations. To capture this statistical structure we generate a point process by thresholding a stochastic process that is continuous in space and discrete in time. This stochastic process is obtained by filtering Gaussian noise through a multivariate autoregressive (AR) model. The parameters of the AR model are obtained by a nonlinear transformation of the point-process correlations to the continuous-process correlations. The proposed method is very efficient and allows for the simulation of large neural populations. It can be optimized to the structure of spatiotemporal correlations and generalized to nonstationary processes and spatiotemporal patterns of local field potentials and spike trains.

  16. Hemispheric Asymmetries in Repetition Enhancement and Suppression Effects in the Newborn Brain.

    Directory of Open Access Journals (Sweden)

    Camillia Bouchon

    Full Text Available The repeated presentation of stimuli typically attenuates neural responses (repetition suppression or, less commonly, increases them (repetition enhancement when stimuli are highly complex, degraded or presented under noisy conditions. In adult functional neuroimaging research, these repetition effects are considered as neural correlates of habituation. The development and respective functional significance of these effects in infancy remain largely unknown.This study investigates repetition effects in newborns using functional near-infrared spectroscopy, and specifically the role of stimulus complexity in evoking a repetition enhancement vs. a repetition suppression response, following up on Gervain et al. (2008. In that study, abstract rule-learning was found at birth in cortical areas specific to speech processing, as evidenced by a left-lateralized repetition enhancement of the hemodynamic response to highly variable speech sequences conforming to a repetition-based ABB artificial grammar, but not to a random ABC grammar.Here, the same paradigm was used to investigate how simpler stimuli (12 different sequences per condition as opposed to 140, and simpler presentation conditions (blocked rather than interleaved would influence repetition effects at birth.Results revealed that the two grammars elicited different dynamics in the two hemispheres. In left fronto-temporal areas, we reproduce the early perceptual discrimination of the two grammars, with ABB giving rise to a greater response at the beginning of the experiment than ABC. In addition, the ABC grammar evoked a repetition enhancement effect over time, whereas a stable response was found for the ABB grammar. Right fronto-temporal areas showed neither initial discrimination, nor change over time to either pattern.Taken together with Gervain et al. (2008, this is the first evidence that manipulating methodological factors influences the presence or absence of neural repetition enhancement

  17. Digital repetitive control under varying frequency conditions

    OpenAIRE

    Ramos Fuentes, Germán Andrés

    2012-01-01

    The tracking/rejection of periodic signals constitutes a wide field of research in the control theory and applications area and Repetitive Control has proven to be an efficient way to face this topic; however, in some applications the period of the signal to be tracked/rejected changes in time or is uncertain, which causes and important performance degradation in the standard repetitive controller. This thesis presents some contributions to the open topic of repetitive control workin...

  18. Dopaminergic modulation of short-term synaptic plasticity in fast-spiking interneurons of primate dorsolateral prefrontal cortex.

    Science.gov (United States)

    Gonzalez-Burgos, G; Kroener, S; Seamans, J K; Lewis, D A; Barrionuevo, G

    2005-12-01

    Dopaminergic regulation of primate dorsolateral prefrontal cortex (PFC) activity is essential for cognitive functions such as working memory. However, the cellular mechanisms of dopamine neuromodulation in PFC are not well understood. We have studied the effects of dopamine receptor activation during persistent stimulation of excitatory inputs onto fast-spiking GABAergic interneurons in monkey PFC. Stimulation at 20 Hz induced short-term excitatory postsynaptic potential (EPSP) depression. The D1 receptor agonist SKF81297 (5 microM) significantly reduced the amplitude of the first EPSP but not of subsequent responses in EPSP trains, which still displayed significant depression. Dopamine (DA; 10 microM) effects were similar to those of SKF81297 and were abolished by the D1 antagonist SCH23390 (5 microM), indicating a D1 receptor-mediated effect. DA did not alter miniature excitatory postsynaptic currents, suggesting that its effects were activity dependent and presynaptic action potential dependent. In contrast to previous findings in pyramidal neurons, in fast-spiking cells, contribution of N-methyl-D-aspartate receptors to EPSPs at subthreshold potentials was not significant and fast-spiking cell depolarization decreased EPSP duration. In addition, DA had no significant effects on temporal summation. The selective decrease in the amplitude of the first EPSP in trains delivered every 10 s suggests that in fast-spiking neurons, DA reduces the amplitude of EPSPs evoked at low frequency but not of EPSPs evoked by repetitive stimulation. DA may therefore improve detection of EPSP bursts above background synaptic activity. EPSP bursts displaying short-term depression may transmit spike-timing-dependent temporal codes contained in presynaptic spike trains. Thus DA neuromodulation may increase the signal-to-noise ratio at fast-spiking cell inputs.

  19. [Repetition and fear of dying].

    Science.gov (United States)

    Lerner, B D

    1995-03-01

    In this paper a revision is made of the qualifications of Repetition (R) in Freuds work, i.e. its being at the service of the Pleasure Principle and, beyond it, the binding of free energy due to trauma. Freud intends to explain with this last concept the "fort-da" and the traumatic dreams (obsessively reiterated self-reproaches may be added to them). The main thesis of this work is that R. is not only a defense against the recollection of the ominous past (as in the metaphorical deaths of abandonment and desertion) but also a way of maintaining life and identify fighting against the inescapable omninous future (known but yet experienced), i.e. our own death. Some forms of R. like habits, identificatory behaviors and sometimes even magic, are geared to serve the life instinct. A literary illustration shows this desperate fight.

  20. Pressure rig for repetitive casting

    Science.gov (United States)

    Vasquez, Peter (Inventor); Hutto, William R. (Inventor); Philips, Albert R. (Inventor)

    1989-01-01

    The invention is a pressure rig for repetitive casting of metal. The pressure rig performs like a piston for feeding molten metal into a mold. Pressure is applied to an expandable rubber diaphragm which expands like a balloon to force the metal into the mold. A ceramic cavity which holds molten metal is lined with blanket-type insulating material, necessitating only a relining for subsequent use and eliminating the lengthy cavity preparation inherent in previous rigs. In addition, the expandable rubber diaphragm is protected by the insulating material thereby decreasing its vulnerability to heat damage. As a result of the improved design the life expectancy of the pressure rig contemplated by the present invention is more than doubled. Moreover, the improved heat protection has allowed the casting of brass and other alloys with higher melting temperatures than possible in the conventional pressure rigs.

  1. Effect of Music on Interictal Epileptiform Spikes

    OpenAIRE

    J Gordon Millichap

    2004-01-01

    The effect of listening to Mozart’s Sonata for Two Pianos (K448) on the frequency of interictal epileptiform discharges (IEDs) in the EEGs of four children, ages 5 – 9 years, with benign childhood epilepsy with centrotemporal spikes (BCECTS) was studied in a prospective, randomized, crossover, placebo-controlled trial at the Medical University of South Carolina, Charleston, SC.

  2. Effect of Music on Interictal Epileptiform Spikes

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2004-10-01

    Full Text Available The effect of listening to Mozart’s Sonata for Two Pianos (K448 on the frequency of interictal epileptiform discharges (IEDs in the EEGs of four children, ages 5 – 9 years, with benign childhood epilepsy with centrotemporal spikes (BCECTS was studied in a prospective, randomized, crossover, placebo-controlled trial at the Medical University of South Carolina, Charleston, SC.

  3. Physics of volleyball: Spiking with a purpose

    Science.gov (United States)

    Behroozi, F.

    1998-05-01

    A few weeks ago our volleyball coach telephoned me with a problem: How high should a player jump to "spike" a "set" ball so it would clear the net and land at a known distance on the other side of the net?

  4. Some Applications of Spiking Neural P Systems

    OpenAIRE

    Mihai Ionescu; Dragoş Sburlan

    2012-01-01

    In this paper we investigate some applications of spiking neural P systems regarding their capability to solve some classical computer science problems. In this respect versatility of such systems is studied to simulate a well known parallel computational model, namely the Boolean circuits. In addition, another notorious application -- sorting -- is considered within this framework.

  5. An Unsupervised Online Spike-Sorting Framework.

    Science.gov (United States)

    Knieling, Simeon; Sridharan, Kousik S; Belardinelli, Paolo; Naros, Georgios; Weiss, Daniel; Mormann, Florian; Gharabaghi, Alireza

    2016-08-01

    Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.

  6. JFK in Blackface: Spike Lee's "Malcolm X."

    Science.gov (United States)

    Walker, Clarence E.

    1993-01-01

    Discusses the failure of filmmaker Spike Lee to grapple with the real politics of Malcolm X before and after he left the Nation of Islam. Acknowledging the complexity of the man and his context would avoid creating a mythical figure similar to Oliver Stone's movie "JFK." (SLD)

  7. STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron.

    Science.gov (United States)

    Luthman, Johannes; Hoebeek, Freek E; Maex, Reinoud; Davey, Neil; Adams, Rod; De Zeeuw, Chris I; Steuber, Volker

    2011-12-01

    Neurons in the cerebellar nuclei (CN) receive inhibitory inputs from Purkinje cells in the cerebellar cortex and provide the major output from the cerebellum, but their computational function is not well understood. It has recently been shown that the spike activity of Purkinje cells is more regular than previously assumed and that this regularity can affect motor behaviour. We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. For high convergence ratios, the irregularity induced spike rate acceleration depends on short-term depression (STD) at the Purkinje cell synapses. At low convergence ratios, or for synchronised Purkinje cell input, the firing rate increase is independent of STD. The transformation of input irregularity into output spike rate occurs in response to artificial input spike trains as well as to spike trains recorded from Purkinje cells in tottering mice, which show highly irregular spiking patterns. Our results suggest that STD may contribute to the accelerated CN spike rate in tottering mice and they raise the possibility that the deficits in motor control in these mutants partly result as a pathological consequence of this natural form of plasticity.

  8. Environmental conditions associated with repetitive behavior in a group of African elephants.

    Science.gov (United States)

    Hasenjager, Matthew J; Bergl, Richard A

    2015-01-01

    Repetitive movement patterns are commonly observed in zoo elephants. The extent to which these behaviors constitute a welfare concern varies, as their expression ranges from stereotypies to potentially beneficial anticipatory behaviors. Nevertheless, their occurrence in zoo animals is often viewed negatively. To better identify conditions that prompt their performance, observations were conducted on six African elephants (Loxodonta africana) at the North Carolina Zoo. Individuals spent most of their time engaged in feeding, locomotion, resting, and repetitive behavior. Both generalized estimating equation and zero-inflated negative binomial models were used to identify factors associated with increased rates of repetitive behavior. Time of day in conjunction with location on- or off-exhibit best explained patterns of repetitive behavior. Repetitive behaviors occurred at a lower rate in the morning when on-exhibit, as compared to afternoons on-exhibit or at any time of day off-exhibit. Increased repetitive behavior rates observed on-exhibit in the afternoon prior to the evening transfer and feeding were possibly anticipatory responses towards those events. In contrast, consistently elevated frequencies of repetitive behavior off-exhibit at all times of day could be related to differences in exhibit complexity between off-exhibit and on-exhibit areas, as well as a lack of additional foraging opportunities. Our study contributes valuable information on captive elephant behavior and represents a good example of how behavioral research can be employed to improve management of zoo animals.

  9. Repetitive negative thinking and suicide: a burgeoning literature with need for further exploration.

    Science.gov (United States)

    Law, Keyne C; Tucker, Raymond P

    2017-08-24

    Extant research has found a significant overlap between various repetitive negative thinking (RNT) patterns, such as rumination and worry, across different affective disorders implicating that the process of repetitive negative thinking is likely trans-diagnostic. Furthermore, RNT patterns at the core of psychiatric disorders associated with suicide (e.g., rumination and worry) have been found to be associated with suicide even after accounting for the disorder. A synthesis of existing literature on repetitive negative thoughts suggest that following negative emotional experiences, RNTs may lead to a sense of entrapment and hopelessness that may contribute to the onset of suicidal ideation and then facilitate the transition from thinking about suicide to making a suicide attempt by increasing an individual's capability for suicide through repetitive exposure to violent thoughts and imagery associated with suicide. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Detecting dependencies between spike trains of pairs of neurons through copulas

    DEFF Research Database (Denmark)

    Sacerdote, Laura; Tamborrino, Massimiliano; Zucca, Cristina

    2011-01-01

    The dynamics of a neuron are influenced by the connections with the network where it lies. Recorded spike trains exhibit patterns due to the interactions between neurons. However, the structure of the network is not known. A challenging task is to investigate it from the analysis of simultaneously...... recorded spike trains. We develop a non-parametric method based on copulas, that we apply to simulated data according to different bivariate Leaky In- tegrate and Fire models. The method discerns dependencies determined by the surround- ing network, from those determined by direct interactions between...

  11. Power-Law Dynamics of Membrane Conductances Increase Spiking Diversity in a Hodgkin-Huxley Model.

    Science.gov (United States)

    Teka, Wondimu; Stockton, David; Santamaria, Fidel

    2016-03-01

    We studied the effects of non-Markovian power-law voltage dependent conductances on the generation of action potentials and spiking patterns in a Hodgkin-Huxley model. To implement slow-adapting power-law dynamics of the gating variables of the potassium, n, and sodium, m and h, conductances we used fractional derivatives of order η≤1. The fractional derivatives were used to solve the kinetic equations of each gate. We systematically classified the properties of each gate as a function of η. We then tested if the full model could generate action potentials with the different power-law behaving gates. Finally, we studied the patterns of action potential that emerged in each case. Our results show the model produces a wide range of action potential shapes and spiking patterns in response to constant current stimulation as a function of η. In comparison with the classical model, the action potential shapes for power-law behaving potassium conductance (n gate) showed a longer peak and shallow hyperpolarization; for power-law activation of the sodium conductance (m gate), the action potentials had a sharp rise time; and for power-law inactivation of the sodium conductance (h gate) the spikes had wider peak that for low values of η replicated pituitary- and cardiac-type action potentials. With all physiological parameters fixed a wide range of spiking patterns emerged as a function of the value of the constant input current and η, such as square wave bursting, mixed mode oscillations, and pseudo-plateau potentials. Our analyses show that the intrinsic memory trace of the fractional derivative provides a negative feedback mechanism between the voltage trace and the activity of the power-law behaving gate variable. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.

  12. Power-Law Dynamics of Membrane Conductances Increase Spiking Diversity in a Hodgkin-Huxley Model.

    Directory of Open Access Journals (Sweden)

    Wondimu Teka

    2016-03-01

    Full Text Available We studied the effects of non-Markovian power-law voltage dependent conductances on the generation of action potentials and spiking patterns in a Hodgkin-Huxley model. To implement slow-adapting power-law dynamics of the gating variables of the potassium, n, and sodium, m and h, conductances we used fractional derivatives of order η≤1. The fractional derivatives were used to solve the kinetic equations of each gate. We systematically classified the properties of each gate as a function of η. We then tested if the full model could generate action potentials with the different power-law behaving gates. Finally, we studied the patterns of action potential that emerged in each case. Our results show the model produces a wide range of action potential shapes and spiking patterns in response to constant current stimulation as a function of η. In comparison with the classical model, the action potential shapes for power-law behaving potassium conductance (n gate showed a longer peak and shallow hyperpolarization; for power-law activation of the sodium conductance (m gate, the action potentials had a sharp rise time; and for power-law inactivation of the sodium conductance (h gate the spikes had wider peak that for low values of η replicated pituitary- and cardiac-type action potentials. With all physiological parameters fixed a wide range of spiking patterns emerged as a function of the value of the constant input current and η, such as square wave bursting, mixed mode oscillations, and pseudo-plateau potentials. Our analyses show that the intrinsic memory trace of the fractional derivative provides a negative feedback mechanism between the voltage trace and the activity of the power-law behaving gate variable. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.

  13. Accurate Prediction of the Statistics of Repetitions in Random Sequences: A Case Study in Archaea Genomes.

    Science.gov (United States)

    Régnier, Mireille; Chassignet, Philippe

    2016-01-01

    Repetitive patterns in genomic sequences have a great biological significance and also algorithmic implications. Analytic combinatorics allow to derive formula for the expected length of repetitions in a random sequence. Asymptotic results, which generalize previous works on a binary alphabet, are easily computable. Simulations on random sequences show their accuracy. As an application, the sample case of Archaea genomes illustrates how biological sequences may differ from random sequences.

  14. Finite-Repetition threshold for infinite ternary words

    Directory of Open Access Journals (Sweden)

    Golnaz Badkobeh

    2011-08-01

    Full Text Available The exponent of a word is the ratio of its length over its smallest period. The repetitive threshold r(a of an a-letter alphabet is the smallest rational number for which there exists an infinite word whose finite factors have exponent at most r(a. This notion was introduced in 1972 by Dejean who gave the exact values of r(a for every alphabet size a as it has been eventually proved in 2009. The finite-repetition threshold for an a-letter alphabet refines the above notion. It is the smallest rational number FRt(a for which there exists an infinite word whose finite factors have exponent at most FRt(a and that contains a finite number of factors with exponent r(a. It is known from Shallit (2008 that FRt(2=7/3. With each finite-repetition threshold is associated the smallest number of r(a-exponent factors that can be found in the corresponding infinite word. It has been proved by Badkobeh and Crochemore (2010 that this number is 12 for infinite binary words whose maximal exponent is 7/3. We show that FRt(3=r(3=7/4 and that the bound is achieved with an infinite word containing only two 7/4-exponent words, the smallest number. Based on deep experiments we conjecture that FRt(4=r(4=7/5. The question remains open for alphabets with more than four letters. Keywords: combinatorics on words, repetition, repeat, word powers, word exponent, repetition threshold, pattern avoidability, word morphisms.

  15. Effects of visual stimulation on LFPs, spikes, and LFP-spike relations in PRR.

    Science.gov (United States)

    Hwang, Eun Jung; Andersen, Richard A

    2011-04-01

    Local field potentials (LFPs) have shown diverse relations to the spikes across different brain areas and stimulus features, suggesting that LFP-spike relationships are highly specific to the underlying connectivity of a local network. If so, the LFP-spike relationship may vary even within one brain area under the same task condition if neurons have heterogeneous connectivity with the active input sources during the task. Here, we tested this hypothesis in the parietal reach region (PRR), which includes two distinct classes of motor goal planning neurons with different connectivity to the visual input, i.e., visuomotor neurons receive stronger visual input than motor neurons. We predicted that the visual stimulation would render both the spike response and the LFP-spike relationship different between the two neuronal subpopulations. Thus we examined how visual stimulations affect spikes, LFPs, and LFP-spike relationships in PRR by comparing their planning (delay) period activity between two conditions: with or without a visual stimulus at the reach target. Neurons were classified as visuomotor if the visual stimulation increased their firing rate, or as motor otherwise. We found that the visual stimulation increased LFP power in gamma bands >40 Hz for both classes. Moreover, confirming our prediction, the correlation between the LFP gamma power and the firing rate became higher for the visuomotor than motor neurons in the presence of visual stimulation. We conclude that LFPs vary with the stimulation condition and that the LFP-spike relationship depends on a given neuron's connectivity to the dominant input sources in a particular stimulation condition.

  16. Rotational Angles and Velocities During Down the Line and Diagonal Across Court Volleyball Spikes

    Directory of Open Access Journals (Sweden)

    Justin R. Brown

    2014-05-01

    Full Text Available The volleyball spike is an explosive movement that is frequently used to end a rally and earn a point. High velocity spikes are an important skill for a successful volleyball offense. Although the influence of vertical jump height and arm velocity on spiked ball velocity (SBV have been investigated, little is known about the relationship of shoulder and hip angular kinematics with SBV. Other sport skills, like the baseball pitch share similar movement patterns and suggest trunk rotation is important for such movements. The purpose of this study was to examine the relationship of both shoulder and hip angular kinematics with ball velocity during the volleyball spike. Methods: Fourteen Division I collegiate female volleyball players executed down the line (DL and diagonally across-court (DAC spikes in a laboratory setting to measure shoulder and hip angular kinematics and velocities. Each spike was analyzed using a 10 Camera Raptor-E Digital Real Time Camera System.  Results: DL SBV was significantly greater than for DAC, respectively (17.54±2.35 vs. 15.97±2.36 m/s, p<0.05.  The Shoulder Hip Separation Angle (S-HSA, Shoulder Angular Velocity (SAV, and Hip Angular Velocity (HAV were all significantly correlated with DAC SBV. S-HSA was the most significant predictor of DAC SBV as determined by regression analysis.  Conclusions: This study provides support for a relationship between a greater S-HSA and SBV. Future research should continue to 1 examine the influence of core training exercise and rotational skill drills on SBV and 2 examine trunk angular velocities during various types of spikes during play.

  17. Maladaptive Repetition and Career Construction

    Science.gov (United States)

    Cardoso, Paulo

    2012-01-01

    The role of repeated maladaptive relationship patterns established early in life has been widely studied with the goal of understanding the various factors and processes that contribute to and occur during development and psychological adaptation. The effects of maladaptive patterns have also been studied with regard to the goal of guiding…

  18. Generalized Volterra kernel model identification of spike-timing-dependent plasticity from simulated spiking activity.

    Science.gov (United States)

    Robinson, Brian S; Song, Dong; Berger, Theodore W

    2014-01-01

    This paper presents a methodology to estimate a learning rule that governs activity-dependent plasticity from behaviorally recorded spiking events. To demonstrate this framework, we simulate a probabilistic spiking neuron with spike-timing-dependent plasticity (STDP) and estimate all model parameters from the simulated spiking data. In the neuron model, output spiking activity is generated by the combination of noise, feedback from the output, and an input-feedforward component whose magnitude is modulated by synaptic weight. The synaptic weight is calculated with STDP with the following features: (1) weight change based on the relative timing of input-output spike pairs, (2) prolonged plasticity induction, and (3) considerations for system stability. Estimation of all model parameters is achieved iteratively by formulating the model as a generalized linear model with Volterra kernels and basis function expansion. Successful estimation of all model parameters in this study demonstrates the feasibility of this approach for in-vivo experimental studies. Furthermore, the consideration of system stability and prolonged plasticity induction enhances the ability to capture how STDP affects a neural population's signal transformation properties over a realistic time course. Plasticity characterization with this estimation method could yield insights into functional implications of STDP and be incorporated into a cortical prosthesis.

  19. Laguerre-Volterra identification of spike-timing-dependent plasticity from spiking activity: a simulation study.

    Science.gov (United States)

    Robinson, Brian S; Song, Dong; Berger, Theodore W

    2013-01-01

    This paper presents a Laguerre-Volterra methodology for identifying a plasticity learning rule from spiking neural data with four components: 1) By analyzing input-output spiking data, the effective contribution of an input on the output firing probability can be quantified with weighted Volterra kernels. 2) The weight of these Volterra kernels can be tracked over time using the stochastic state point processing filtering algorithm (SSPPF) 3) Plasticity system Volterra kernels can be estimated by treating the tracked change in weight over time as the plasticity system output and the spike timing data as the input. 4) Laguerre expansion of all Volterra kernels allows for minimization of open parameters during estimation steps. A single input spiking neuron with Spike-timing-dependent plasticity (STDP) and prolonged STDP induction is simulated. Using the spiking data from this simulation, the amplitude of the STDP learning rule and the time course of the induction is accurately estimated. This framework can be applied to identify plasticity for more complicated plasticity paradigms and is applicable to in vivo data.

  20. Hypersonic wave drag reduction performance of cylinders with repetitive laser energy depositions

    Energy Technology Data Exchange (ETDEWEB)

    Fang, J; Hong, Y J; Li, Q; Huang, H, E-mail: fangjuan314@163.com [Academy of Equipment Command and Technology, Post Box 3380-86, Huairou Dis. Beijing 101416 (China)

    2011-02-01

    It has been widely research that wave drag reduction on hypersonic vehicle by laser energy depositions. Using laser energy to reduce wave drag can improve vehicle performance. A second order accurate scheme based on finite-difference method and domain decomposition of structural grid is used to compute the drag performance of cylinders in a hypersonic flow of Mach number 2 at altitude of 15km with repetitive energy depositions. The effects of frequency on drag reduction are studied. The calculated results show: the recirculation zone is generated due to the interaction between bow shock over the cylinder and blast wave produced by energy deposition, and a virtual spike which is supported by an axis-symmetric recirculation, is formed in front of the cylinder. By increasing the repetitive frequency, the drag is reduced and the oscillation of the drag is decreased; however, the energy efficiency decreases by increasing the frequency.

  1. Comparing repetition-based melody segmentation models

    NARCIS (Netherlands)

    Rodríguez López, M.E.; de Haas, Bas; Volk, Anja

    2014-01-01

    This paper reports on a comparative study of computational melody segmentation models based on repetition detection. For the comparison we implemented five repetition-based segmentation models, and subsequently evaluated their capacity to automatically find melodic phrase boundaries in a corpus of 2

  2. Task Repetition and Second Language Speech Processing

    Science.gov (United States)

    Lambert, Craig; Kormos, Judit; Minn, Danny

    2017-01-01

    This study examines the relationship between the repetition of oral monologue tasks and immediate gains in L2 fluency. It considers the effect of aural-oral task repetition on speech rate, frequency of clause-final and midclause filled pauses, and overt self-repairs across different task types and proficiency levels and relates these findings to…

  3. Repetitions: A Cross-Cultural Study.

    Science.gov (United States)

    Murata, Kumiko

    1995-01-01

    This study investigated how repetition is used in conversation among native speakers of British English, native speakers of Japanese, and Japanese speakers of English. Five interactional functions of repetition (interruption-orientated, solidarity, silence-avoidance, hesitation, and reformulation) were identified, as well as the cultural factors…

  4. The firing patterns of spinal neurons: in situ patch-clamp recordings reveal a key role for potassium currents.

    Science.gov (United States)

    Winlove, Crawford I P; Roberts, Alan

    2012-10-01

    Neuron firing patterns underpin the detection and processing of stimuli, influence synaptic interactions, and contribute to the function of networks. To understand how intrinsic membrane properties determine firing patterns, we investigated the biophysical basis of single and repetitive firing in spinal neurons of hatchling Xenopus laevis tadpoles, a well-understood vertebrate model; experiments were conducted in situ. Primary sensory Rohon-Beard (RB) neurons fire singly in response to depolarising current, and dorsolateral (DL) interneurons fire repetitively. RB neurons exhibited a large tetrodotoxin-sensitive sodium current; in DL neurons, the sodium current density was significantly lower. High-voltage-activated calcium currents were similar in both neuron types. There was no evidence of persistent sodium currents, low-voltage-activated calcium currents, or hyperpolarisation-activated currents. In RB neurons, the potassium current was dominated by a tetraethylammonium-sensitive slow component (I(Ks) ); a fast component (I(Kf) ), sensitive to 4-aminopyridine, predominated in DL neurons. Sequential current-clamp and voltage-clamp recordings in individual neurons suggest that high densities of I(Ks) prevent repetitive firing; where I(Ks) is small, I(Kf) density determines the frequency of repetitive firing. Intermediate densities of I(Ks) and I(Kf) allow neurons to fire a few additional spikes on strong depolarisation; this property typifies a novel subset of RB neurons, and may activate escape responses. We discuss how this ensemble of currents and firing patterns underpins the operation of the Xenopus locomotor network, and suggest how simple mechanisms might underlie the similar firing patterns seen in the neurons of diverse species.

  5. Skill learning and repetition priming in Alzheimer's disease.

    Science.gov (United States)

    Grober, E; Ausubel, R; Sliwinski, M; Gordon, B

    1992-10-01

    While perceptual-motor learning occurs normally in Alzheimer's disease (AD) patients, their ability to acquire the skill of reading transformed text has not been well delineated. AD patients and matched controls were timed as they read two blocks of words presented in mirror image. Control subjects displayed both skill learning and repetition priming, whereas AD patients displayed only repetition priming. Skill learning in AD patients was associated with their ability to complete verbal analogies. They displayed the expected impairment in recognition for the words from the mirror reading task. The failure of AD patients to acquire the mirror reading skill can be understood through a task analysis and may reflect an underlying deficit in abstract reasoning that precludes the development of appropriate pattern analyzing strategies needed to transform rotated text.

  6. Digital repetitive control under varying frequency conditions

    CERN Document Server

    Ramos, Germán A; Olm, Josep M

    2013-01-01

    The tracking/rejection of periodic signals constitutes a wide field of research in the control theory and applications area. Repetitive Control has proven to be an efficient way to face this topic. However, in some applications the frequency of the reference/disturbance signal is time-varying or uncertain. This causes an important performance degradation in the standard Repetitive Control scheme. This book presents some solutions to apply Repetitive Control in varying frequency conditions without loosing steady-state performance. It also includes a complete theoretical development and experimental results in two representative systems. The presented solutions are organized in two complementary branches: varying sampling period Repetitive Control and High Order Repetitive Control. The first approach allows dealing with large range frequency variations while the second allows dealing with small range frequency variations. The book also presents applications of the described techniques to a Roto-magnet plant and...

  7. Age-Related Differences in Restricted Repetitive Behaviors in Autism Spectrum Disorders

    Science.gov (United States)

    Esbensen, Anna J.; Seltzer, Marsha Mailick; Lam, Kristen S. L.; Bodfish, James W.

    2009-01-01

    Restricted repetitive behaviors (RRBs) were examined in a large group of children, adolescents and adults with ASD in order to describe age-related patterns of symptom change and association with specific contextual factors, and to examine if the patterns of change are different for the various types of RRBs. Over 700 individuals with ASD were…

  8. A generative spike train model with time-structured higher order correlations

    Directory of Open Access Journals (Sweden)

    James eTrousdale

    2013-07-01

    Full Text Available Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem.Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS model creates marginally Poisson spike trains with diverse temporal correlation structures.We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs.We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics.

  9. The algorithmic complexity of neural spike trains increases during focal seizures.

    Science.gov (United States)

    Rapp, P E; Zimmerman, I D; Vining, E P; Cohen, N; Albano, A M; Jiménez-Montaño, M A

    1994-08-01

    The interspike interval spike trains of spontaneously active cortical neurons can display nonrandom internal structure. The degree of nonrandom structure can be quantified and was found to decrease during focal epileptic seizures. Greater statistical discrimination between the two physiological conditions (normal vs seizure) was obtained with measurements of context-free grammar complexity than by measures of the distribution of the interspike intervals such as the mean interval, its standard deviation, skewness, or kurtosis. An examination of fixed epoch data sets showed that two factors contribute to the complexity: the firing rate and the internal structure of the spike train. However, calculations with randomly shuffled surrogates of the original data sets showed that the complexity is not completely determined by the firing rate. The sequence-sensitive structure of the spike train is a significant contributor. By combining complexity measurements with statistically related surrogate data sets, it is possible to classify neurons according to the dynamical structure of their spike trains. This classification could not have been made on the basis of conventional distribution-determined measures. Computations with more sophisticated kinds of surrogate data show that the structure observed using complexity measures cannot be attributed to linearly correlated noise or to linearly correlated noise transformed by a static monotonic nonlinearity. The patterns in spike trains appear to reflect genuine nonlinear structure. The limitations of these results are also discussed. The results presented in this article do not, of themselves, establish the presence of a fine-structure encoding of neural information.

  10. Energy demands of diverse spiking cells from the neocortex, hippocampus and thalamus.

    Directory of Open Access Journals (Sweden)

    Abdelmalik eMoujahid

    2014-04-01

    Full Text Available It has long been known that neurons in the brain are not physiologically homogeneous. In response to current stimulus, they can fire several distinct patterns of action potentials that are associated with different physiological classes ranging from regular-spiking cells, fast-spiking cells, intrinsically bursting cells, and low-threshold cells. In this work we show that the high degree of variability in firing characteristics of action potentials among these cells is accompanied with a significant variability in the energy demands required to restore the concentration gradients after an action potential.The values of the metabolic energy were calculated for a wide range of cell temperatures and stimulus intensities following two different approaches. The first one is based on the amount of Na$^+$ load crossing the membrane during a single action potential, while the second one focuses on the electrochemical energy functions deduced from the dynamics of the computational neuron models. The results show that the thalamocortical relay neuron is the most energy-efficient cell consuming between 7 to 18 nJ/cm$^2$ for each spike generated, while both the regular and fast spiking cells from somatosensory cortex and the intrinsically-bursting cell from a cat visual cortex are the least energy-efficient, and can consume up to 100 nJ/cm$^2$ per spike. The lowest values of these energy demands were achieved at higher temperatures and high external stimuli.

  11. Spatiotemporal Mapping of Interictal Spike Propagation: A Novel Methodology Applied to Pediatric Intracranial EEG Recordings.

    Science.gov (United States)

    Tomlinson, Samuel B; Bermudez, Camilo; Conley, Chiara; Brown, Merritt W; Porter, Brenda E; Marsh, Eric D

    2016-01-01

    Synchronized cortical activity is implicated in both normative cognitive functioning and many neurologic disorders. For epilepsy patients with intractable seizures, irregular synchronization within the epileptogenic zone (EZ) is believed to provide the network substrate through which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical for detecting seizure networks in order to achieve postsurgical seizure control. However, automated techniques for characterizing epileptic networks have yet to gain traction in the clinical setting. Recent advances in signal processing and spike detection have made it possible to examine the spatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, we present a novel methodology for detecting, extracting, and visualizing spike propagation and demonstrate its potential utility as a biomarker for the EZ. Eighteen presurgical intracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable (i.e., seizure-free, n = 9) or unfavorable (i.e., seizure-persistent, n = 9) surgical outcomes. Novel algorithms were applied to extract multichannel spike discharges and visualize their spatiotemporal propagation. Quantitative analysis of spike propagation was performed using trajectory clustering and spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed an increase in trajectory organization (i.e., spatial autocorrelation) among Sz-Free patients compared with Sz-Persist patients. The pathophysiological basis and clinical implications of these findings are considered.

  12. Spiking in auditory cortex following thalamic stimulation is dominated by cortical network activity

    Directory of Open Access Journals (Sweden)

    Bryan M Krause

    2014-09-01

    Full Text Available The state of the sensory cortical network can have a profound impact on neural responses and perception. In rodent auditory cortex, sensory responses are reported to occur in the context of network events, similar to brief UP states, that produce 'packets' of spikes and are associated with synchronized synaptic input (Bathellier et al., 2012; Hromadka et al., 2013; Luczak et al., 2013. However, traditional models based on data from visual and somatosensory cortex predict that ascending sensory thalamocortical (TC pathways sequentially activate cells in layers 4 (L4, L2/3 and L5. The relationship between these two spatio-temporal activity patterns is unclear. Here, we used calcium imaging and electrophysiological recordings in murine auditory TC brain slices to investigate the laminar response pattern to stimulation of TC afferents. We show that although monosynaptically driven spiking in response to TC afferents occurs, the vast majority of spikes fired following TC stimulation occurs during brief UP states and outside the context of the L4>L2/3>L5 activation sequence. Specifically, monosynaptic subthreshold TC responses with similar latencies were observed throughout layers 2 - 6, presumably via synapses onto dendritic processes located in L3 & L4. However, monosynaptic spiking was rare, and occurred primarily in L4 and L5 non-pyramidal cells. By contrast, during brief, TC-induced UP states, spiking was dense and occurred primarily in pyramidal cells. These network events always involved infragranular layers, whereas involvement of supragranular layers was variable. During UP states, spike latencies were comparable between infragranular and supragranular cells. These data are consistent with a model in which activation of auditory cortex, especially supragranular layers, depends on internally generated network events that represent a nonlinear amplification process, are initiated by infragranular cells and tightly regulated by feed

  13. Strategies for Using Repetition as a Powerful Teaching Tool

    Science.gov (United States)

    Saville, Kirt

    2011-01-01

    Brain research indicates that repetition is of vital importance in the learning process. Repetition is an especially useful tool in the area of music education. The success of repetition can be enhanced by accurate and timely feedback. From "simple repetition" to "repetition with the addition or subtraction of degrees of freedom," there are many…

  14. Strategies for Using Repetition as a Powerful Teaching Tool

    Science.gov (United States)

    Saville, Kirt

    2011-01-01

    Brain research indicates that repetition is of vital importance in the learning process. Repetition is an especially useful tool in the area of music education. The success of repetition can be enhanced by accurate and timely feedback. From "simple repetition" to "repetition with the addition or subtraction of degrees of freedom," there are many…

  15. The Mutation Frequency in Different Spike Categories in Barley

    DEFF Research Database (Denmark)

    Frydenberg, O.; Doll, Hans; Sandfær, J.

    1964-01-01

    After gamma irradiation of barley seeds, a comparison has been made between the chlorophyll-mutant frequencies in X1 spikes that had multicellular bud meristems in the seeds at the time of treatment (denoted as pre-formed spikes) and X1 spikes having no recognizable meristems at the time...

  16. Repetition priming from moving faces.

    Science.gov (United States)

    Lander, Karen; Bruce, Vicki

    2004-06-01

    Recent experiments have suggested that seeing a familiar face move provides additional dynamic information to the viewer, useful in the recognition of identity. In four experiments, repetition priming was used to investigate whether dynamic information is intrinsic to the underlying face representations. The results suggest that a moving image primes more effectively than a static image, even when the same static image is shown in the prime and the test phases (Experiment 1). Furthermore, when moving images are presented in the test phase (Experiment 2), there is an advantage for moving prime images. The most priming advantage is found with naturally moving faces, rather than with those shown in slow motion (Experiment 3). Finally, showing the same moving sequence at prime and test produced more priming than that found when different moving sequences were shown (Experiment 4). The results suggest that dynamic information is intrinsic to the face representations and that there is an advantage to viewing the same moving sequence at prime and test.

  17. Collision-spike Sputtering of Au Nanoparticles.

    Science.gov (United States)

    Sandoval, Luis; Urbassek, Herbert M

    2015-12-01

    Ion irradiation of nanoparticles leads to enhanced sputter yields if the nanoparticle size is of the order of the ion penetration depth. While this feature is reasonably well understood for collision-cascade sputtering, we explore it in the regime of collision-spike sputtering using molecular-dynamics simulation. For the particular case of 200-keV Xe bombardment of Au particles, we show that collision spikes lead to abundant sputtering with an average yield of 397 ± 121 atoms compared to only 116 ± 48 atoms for a bulk Au target. Only around 31 % of the impact energy remains in the nanoparticles after impact; the remainder is transported away by the transmitted projectile and the ejecta. The sputter yield of supported nanoparticles is estimated to be around 80 % of that of free nanoparticles due to the suppression of forward sputtering.

  18. Spiking neural P systems with weights.

    Science.gov (United States)

    Wang, Jun; Hoogeboom, Hendrik Jan; Pan, Linqiang; Păun, Gheorghe; Pérez-Jiménez, Mario J

    2010-10-01

    A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers, integers, and natural numbers. The power of the obtained systems is investigated. For instance, it is proved that integers (very restricted: 1, -1 for weights, 1 and 2 for firing thresholds, and as parameters in the rules) suffice for computing all Turing computable sets of numbers in both the generative and the accepting modes. When only natural numbers are used, a characterization of the family of semilinear sets of numbers is obtained. It is shown that spiking neural P systems with weights can efficiently solve computationally hard problems in a nondeterministic way. Some open problems and suggestions for further research are formulated.

  19. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

    2012-08-01

    Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

  20. Document Retrieval on Repetitive Collections

    OpenAIRE

    Navarro, Gonzalo; Puglisi, Simon J.; Sirén, Jouni

    2014-01-01

    Document retrieval aims at finding the most important documents where a pattern appears in a collection of strings. Traditional pattern-matching techniques yield brute-force document retrieval solutions, which has motivated the research on tailored indexes that offer near-optimal performance. However, an experimental study establishing which alternatives are actually better than brute force, and which perform best depending on the collection characteristics, has not been carried out. In this ...

  1. Maladaptive repetition and career construction

    OpenAIRE

    Cardoso, Paulo

    2012-01-01

    The role of repeated maladaptive relationship patterns established early in life has been widely studied with the goal of understanding the various factors and processes that contribute to and occur during development and psychological adaptation. The effects of maladaptive patterns have also been studied with regard to the goal of guiding interventions. The present article is intended to highlight the contribution of Career Construction Theory (CCT) for the integration of the concept of mala...

  2. Spiked Instantons from Intersecting D-branes

    CERN Document Server

    Nekrasov, Nikita

    2016-01-01

    The moduli space of spiked instantons that arises in the context of the BPS/CFT correspondence is realised as the moduli space of classical vacua, i.e. low-energy open string field configurations, of a certain stack of intersecting D1-branes and D5-branes in Type IIB string theory. The presence of a constant B-field induces an interesting dynamics involving the tachyon condensation.

  3. Spiked instantons from intersecting D-branes

    Directory of Open Access Journals (Sweden)

    Nikita Nekrasov

    2017-01-01

    Full Text Available The moduli space of spiked instantons that arises in the context of the BPS/CFT correspondence [22] is realised as the moduli space of classical vacua, i.e. low-energy open string field configurations, of a certain stack of intersecting D1-branes and D5-branes in Type IIB string theory. The presence of a constant B-field induces an interesting dynamics involving the tachyon condensation.

  4. Spiked instantons from intersecting D-branes

    Science.gov (United States)

    Nekrasov, Nikita; Prabhakar, Naveen S.

    2017-01-01

    The moduli space of spiked instantons that arises in the context of the BPS/CFT correspondence [22] is realised as the moduli space of classical vacua, i.e. low-energy open string field configurations, of a certain stack of intersecting D1-branes and D5-branes in Type IIB string theory. The presence of a constant B-field induces an interesting dynamics involving the tachyon condensation.

  5. Stochastic synchronization in finite size spiking networks

    Science.gov (United States)

    Doiron, Brent; Rinzel, John; Reyes, Alex

    2006-09-01

    We study a stochastic synchronization of spiking activity in feedforward networks of integrate-and-fire model neurons. A stochastic mean field analysis shows that synchronization occurs only when the network size is sufficiently small. This gives evidence that the dynamics, and hence processing, of finite size populations can be drastically different from that observed in the infinite size limit. Our results agree with experimentally observed synchrony in cortical networks, and further strengthen the link between synchrony and propagation in cortical systems.

  6. Analysis of Neuronal Spike Trains, Deconstructed.

    Science.gov (United States)

    Aljadeff, Johnatan; Lansdell, Benjamin J; Fairhall, Adrienne L; Kleinfeld, David

    2016-07-20

    As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods.

  7. Superbubbles, Galactic Dynamos and the Spike Instability

    CERN Document Server

    Kulsrud, Russell M

    2015-01-01

    We draw attention to a problem with the alpha-Omega dynamo when it is applied to the origin of the galactic magnetic field under the assumption of perfect flux freezing. The standard theory involves the expulsion of undesirable flux and, because of flux freezing, the mass anchored on this flux also must be expelled. The strong galactic gravitational field makes this impossible on energetic grounds. It is shown that if only short pieces of the undesirable field lines are expelled, then mass can flow down along these field lines without requiring much energy. This expulsion of only short lines of force can be accomplished by a spike instability associated with gigantic astrophysical superbubbles. The physics of this instability is discussed and the results enable an estimate to be made of the number of spikes in the galaxy. It appears that there are probably enough spikes to cut all the undesirable lines into pieces as short as a couple of kiloparsecs during a dynamo time of a billion years. These cut pieces th...

  8. Bursting and spiking due to additional direct and stochastic currents in neuron models

    Institute of Scientific and Technical Information of China (English)

    Yang Zhuo-Qin; Lu Qi-Shao

    2006-01-01

    Neurons at rest can exhibit diverse firing activities patterns in response to various external deterministic and random stimuli, especially additional currents. In this paper, neuronal firing patterns from bursting to spiking, induced by additional direct and stochastic currents, are explored in rest states Corresponding to two values of the parameter VK in the Chay neuron system. Three cases are considered by numerical simulation and fast/slow dynamic analysis, in which only the direct current or the stochastic current exists, or the direct and stochastic currents coexist. Meanwhile, several important bursting patterns in neuronal experiments, such as the period-1 "circle/homoclinic" bursting and the integer multiple "fold/homoclinic" bursting with one spike per burst, as well as the transition from integer multiple bursting to period-1 "circle/homoclinic" bursting and that from stochastic "Hopf/homoclinic" bursting to "Hopf/homoclinic" bursting, are investigated in detail.

  9. Firing regulation of fast-spiking interneurons by autaptic inhibition

    Science.gov (United States)

    Guo, Daqing; Chen, Mingming; Perc, Matjaž; Wu, Shengdun; Xia, Chuan; Zhang, Yangsong; Xu, Peng; Xia, Yang; Yao, Dezhong

    2016-05-01

    Fast-spiking (FS) interneurons in the brain are self-innervated by powerful inhibitory GABAergic autaptic connections. By computational modelling, we investigate how autaptic inhibition regulates the firing response of such interneurons. Our results indicate that autaptic inhibition both boosts the current threshold for action potential generation and modulates the input-output gain of FS interneurons. The autaptic transmission delay is identified as a key parameter that controls the firing patterns and determines multistability regions of FS interneurons. Furthermore, we observe that neuronal noise influences the firing regulation of FS interneurons by autaptic inhibition and extends their dynamic range for encoding inputs. Importantly, autaptic inhibition modulates noise-induced irregular firing of FS interneurons, such that coherent firing appears at an optimal autaptic inhibition level. Our results reveal the functional roles of autaptic inhibition in taming the firing dynamics of FS interneurons.

  10. Balanced Networks of Spiking Neurons with Spatially Dependent Recurrent Connections

    Science.gov (United States)

    Rosenbaum, Robert; Doiron, Brent

    2014-04-01

    Networks of model neurons with balanced recurrent excitation and inhibition capture the irregular and asynchronous spiking activity reported in cortex. While mean-field theories of spatially homogeneous balanced networks are well understood, a mean-field analysis of spatially heterogeneous balanced networks has not been fully developed. We extend the analysis of balanced networks to include a connection probability that depends on the spatial separation between neurons. In the continuum limit, we derive that stable, balanced firing rate solutions require that the spatial spread of external inputs be broader than that of recurrent excitation, which in turn must be broader than or equal to that of recurrent inhibition. Notably, this implies that network models with broad recurrent inhibition are inconsistent with the balanced state. For finite size networks, we investigate the pattern-forming dynamics arising when balanced conditions are not satisfied. Our study highlights the new challenges that balanced networks pose for the spatiotemporal dynamics of complex systems.

  11. Precision markedly attenuates repetitive lift capacity.

    Science.gov (United States)

    Collier, Brooke R; Holland, Laura; McGhee, Deirdre; Sampson, John A; Bell, Alison; Stapley, Paul J; Groeller, Herbert

    2014-01-01

    This study investigated the effect of precision on time to task failure in a repetitive whole-body manual handling task. Twelve participants were required to repetitively lift a box weighing 65% of their single repetition maximum to shoulder height using either precise or unconstrained box placement. Muscle activity, forces exerted at the ground, 2D body kinematics, box acceleration and psychophysical measures of performance were recorded until task failure was reached. With precision, time to task failure for repetitive lifting was reduced by 72%, whereas the duration taken to complete a single lift and anterior deltoid muscle activation increased by 39% and 25%, respectively. Yet, no significant difference was observed in ratings of perceived exertion or heart rate at task failure. In conclusion, our results suggest that when accuracy is a characteristic of a repetitive manual handling task, physical work capacity will decline markedly. The capacity to lift repetitively to shoulder height was reduced by 72% when increased accuracy was required to place a box upon a shelf. Lifting strategy and muscle activity were also modified, confirming practitioners should take into consideration movement precision when evaluating the demands of repetitive manual handling tasks.

  12. Adaptive robotic control driven by a versatile spiking cerebellar network.

    Science.gov (United States)

    Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Carrillo, Richard R; Luque, Niceto R; Ros, Eduardo; Pedrocchi, Alessandra; D'Angelo, Egidio

    2014-01-01

    The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

  13. Adaptive robotic control driven by a versatile spiking cerebellar network.

    Directory of Open Access Journals (Sweden)

    Claudia Casellato

    Full Text Available The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning, a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

  14. Repetitive sequences in Eurasian lynx (Lynx lynx L.) mitochondrial DNA control region.

    Science.gov (United States)

    Sindičić, Magda; Gomerčić, Tomislav; Galov, Ana; Polanc, Primož; Huber, Duro; Slavica, Alen

    2012-06-01

    Mitochondrial DNA (mtDNA) control region (CR) of numerous species is known to include up to five different repetitive sequences (RS1-RS5) that are found at various locations, involving motifs of different length and extensive length heteroplasmy. Two repetitive sequences (RS2 and RS3) on opposite sides of mtDNA central conserved region have been described in domestic cat (Felis catus) and some other felid species. However, the presence of repetitive sequence RS3 has not been detected in Eurasian lynx (Lynx lynx) yet. We analyzed mtDNA CR of 35 Eurasian lynx (L. lynx L.) samples to characterize repetitive sequences and to compare them with those found in other felid species. We confirmed the presence of 80 base pairs (bp) repetitive sequence (RS2) at the 5' end of the Eurasian lynx mtDNA CR L strand and for the first time we described RS3 repetitive sequence at its 3' end, consisting of an array of tandem repeats five to ten bp long. We found that felid species share similar RS3 repetitive pattern and fundamental repeat motif TACAC.

  15. Branching Shoots and Spikes from Lateral Meristems in Bread Wheat.

    Directory of Open Access Journals (Sweden)

    Ying Wang

    Full Text Available Wheat grain yield consists of three components: spikes per plant, grains per spike (i.e. head or ear, and grain weight; and the grains per spike can be dissected into two subcomponents: spikelets per spike and grains per spikelet. An increase in any of these components will directly contribute to grain yield. Wheat morphology biology tells that a wheat plant has no lateral meristem that forms any branching shoot or spike. In this study, we report two novel shoot and spike traits that were produced from lateral meristems in bread wheat. One is supernumerary shoot that was developed from an axillary bud at the axil of leaves on the elongated internodes of the main stem. The other is supernumerary spike that was generated from a spikelet meristem on a spike. In addition, supernumerary spikelets were generated on the same rachis node of the spike in the plant that had supernumerary shoot and spikes. All of these supernumerary shoots/spikes/spikelets found in the super wheat plants produced normal fertility and seeds, displaying huge yield potential in bread wheat.

  16. Introduction to spiking neural networks: Information processing, learning and applications.

    Science.gov (United States)

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

    The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

  17. Kv3.4 subunits enhance the repolarizing efficiency of Kv3.1 channels in fast-spiking neurons.

    Science.gov (United States)

    Baranauskas, Gytis; Tkatch, Tatiana; Nagata, Keiichi; Yeh, Jay Z; Surmeier, D James

    2003-03-01

    Neurons with the capacity to discharge at high rates--'fast-spiking' (FS) neurons--are critical participants in central motor and sensory circuits. It is widely accepted that K+ channels with Kv3.1 or Kv3.2 subunits underlie fast, delayed-rectifier (DR) currents that endow neurons with this FS ability. Expression of these subunits in heterologous systems, however, yields channels that open at more depolarized potentials than do native Kv3 family channels, suggesting that they differ. One possibility is that native channels incorporate a subunit that modifies gating. Molecular, electrophysiological and pharmacological studies reported here suggest that a splice variant of the Kv3.4 subunit coassembles with Kv3.1 subunits in rat brain FS neurons. Coassembly enhances the spike repolarizing efficiency of the channels, thereby reducing spike duration and enabling higher repetitive spike rates. These results suggest that manipulation of K3.4 subunit expression could be a useful means of controlling the dynamic range of FS neurons.

  18. Repetition suppression: a means to index neural representations using BOLD?

    Science.gov (United States)

    Behrens, Timothy E. J.

    2016-01-01

    Understanding how the human brain gives rise to complex cognitive processes remains one of the biggest challenges of contemporary neuroscience. While invasive recording in animal models can provide insight into neural processes that are conserved across species, our understanding of cognition more broadly relies upon investigation of the human brain itself. There is therefore an imperative to establish non-invasive tools that allow human brain activity to be measured at high spatial and temporal resolution. In recent years, various attempts have been made to refine the coarse signal available in functional magnetic resonance imaging (fMRI), providing a means to investigate neural activity at the meso-scale, i.e. at the level of neural populations. The most widely used techniques include repetition suppression and multivariate pattern analysis. Human neuroscience can now use these techniques to investigate how representations are encoded across neural populations and transformed by relevant computations. Here, we review the physiological basis, applications and limitations of fMRI repetition suppression with a brief comparison to multivariate techniques. By doing so, we show how fMRI repetition suppression holds promise as a tool to reveal complex neural mechanisms that underlie human cognitive function. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574308

  19. Comparison of electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil

    DEFF Research Database (Denmark)

    Ottosen, Lisbeth M.; Lepkova, Katarina; Kubal, Martin

    2006-01-01

    Electrokinetic remediation methods for removal of heavy metals from polluted soils have been subjected for quite intense research during the past years since these methods are well suitable for fine-grained soils where other remediation methods fail. Electrodialytic remediation is an electrokinetic...... polluted soil under the same operational conditions (constant current density 0.2 mA/cm2 and duration 28 days). The results of the present paper show that caution must be taken when generalising results obtained in spiked kaolinite to remediation of industrially polluted soils, as it was shown...... remediation method which is based on applying an electric DC field and the use of ion exchange membranes that ensures the main transport of heavy metals to be out of the pollutes soil. An experimental investigation was made with electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially...

  20. Comparison of electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil

    DEFF Research Database (Denmark)

    Ottosen, Lisbeth M.; Lepkova, Katarina; Kubal, Martin

    2006-01-01

    Electrokinetic remediation methods for removal of heavy metals from polluted soils have been subjected for quite intense research during the past years since these methods are well suitable for fine-grained soils where other remediation methods fail. Electrodialytic remediation is an electrokinetic...... remediation method which is based on applying an electric DC field and the use of ion exchange membranes that ensures the main transport of heavy metals to be out of the pollutes soil. An experimental investigation was made with electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially...... polluted soil under the same operational conditions (constant current density 0.2 mA/cm2 and duration 28 days). The results of the present paper show that caution must be taken when generalising results obtained in spiked kaolinite to remediation of industrially polluted soils, as it was shown...

  1. Repetitive Bibliographical Information in Relational Databases.

    Science.gov (United States)

    Brooks, Terrence A.

    1988-01-01

    Proposes a solution to the problem of loading repetitive bibliographic information in a microcomputer-based relational database management system. The alternative design described is based on a representational redundancy design and normalization theory. (12 references) (Author/CLB)

  2. Computer-Related Repetitive Stress Injuries

    Science.gov (United States)

    ... on the shoulder Epicondylitis: elbow soreness often called "tennis elbow" Ganglion cyst: swelling or lump in the wrist ... Bones, Muscles, and Joints Carpal Tunnel Syndrome Medial Epicondylitis Repetitive Stress Injuries Contact Us Print Resources Send ...

  3. Cortical activity influences geniculocortical spike efficacy in the macaque monkey

    Directory of Open Access Journals (Sweden)

    Farran Briggs

    2007-11-01

    Full Text Available Thalamocortical communication is a dynamic process influenced by both presynaptic and postsynaptic mechanisms. In this study, we recorded single-unit responses from cortical neurons that received direct input from the lateral geniculate nucleus (LGN to address the question of whether prior patterns of cortical activity affect the ability of LGN inputs to drive cortical responses. By examining the ongoing activity that preceded the arrival of electrically evoked spikes from the LGN, we identified a number of activity patterns that were predictive of suprathreshold communication. Namely, cortical neurons were more likely to respond to LGN stimulation when their activity levels increased to 30-40Hz and/or their activity displayed rhythmic patterns (30 ms intervals with increased power in the gamma frequency band. Cortical neurons were also more likely to respond to LGN stimulation when their activity increased 30-40 ms prior to stimulation, suggesting that the phase of gamma activity also contributes to geniculocortical communication. Based on these results, we conclude that ongoing activity in the cortex is not random, but rather organized in a manner that can influence the dynamics of thalamocortical communication.

  4. Complementary contributions of spike timing and spike rate to perceptual decisions in rat S1 and S2 cortex.

    Science.gov (United States)

    Zuo, Yanfang; Safaai, Houman; Notaro, Giuseppe; Mazzoni, Alberto; Panzeri, Stefano; Diamond, Mathew E

    2015-02-02

    When a neuron responds to a sensory stimulus, two fundamental codes [1-6] may transmit the information specifying stimulus identity--spike rate (the total number of spikes in the sequence, normalized by time) and spike timing (the detailed millisecond-scale temporal structure of the response). To assess the functional significance of these codes, we recorded neuronal responses in primary (S1) and secondary (S2) somatosensory cortex of five rats as they used their whiskers to identify textured surfaces. From the spike trains evoked during whisker contact with the texture, we computed the information that rate and timing codes carried about texture identity and about the rat's choice. S1 and S2 spike timing carried more information about stimulus and about choice than spike rates; the conjunction of rate and timing carried more information than either code alone. Moreover, on trials when our spike-timing-decoding algorithm extracted faithful texture information, the rat was more likely to choose correctly; when our spike-timing-decoding algorithm extracted misleading texture information, the rat was more likely to err. For spike rate information, the relationship between faithfulness of the message and correct choice was significant but weaker. These results indicate that spike timing makes crucial contributions to tactile perception, complementing and surpassing those made by rate. The language by which somatosensory cortical neurons transmit information, and the readout mechanism used to produce behavior, appears to rely on multiplexed signals from spike rate and timing.

  5. Digital repetitive control under varying frequency conditions

    OpenAIRE

    Ramos Fuentes, Germán Andrés

    2012-01-01

    Premi extraordinari doctorat curs 2011-2012, àmbit d’Enginyeria Industrial The tracking/rejection of periodic signals constitutes a wide field of research in the control theory and applications area and Repetitive Control has proven to be an efficient way to face this topic; however, in some applications the period of the signal to be tracked/rejected changes in time or is uncertain, which causes and important performance degradation in the standard repetitive controller. This the...

  6. Rotated balance in humans due to repetitive rotational movement.

    Science.gov (United States)

    Zakynthinaki, M S; Milla, J Madera; De Durana, A López Diaz; Martínez, C A Cordente; Romo, G Rodríguez; Quintana, M Sillero; Molinuevo, J Sampedro

    2010-03-01

    We show how asymmetries in the movement patterns during the process of regaining balance after perturbation from quiet stance can be modeled by a set of coupled vector fields for the derivative with respect to time of the angles between the resultant ground reaction forces and the vertical in the anteroposterior and mediolateral directions. In our model, which is an adaption of the model of Stirling and Zakynthinaki (2004), the critical curve, defining the set of maximum angles one can lean to and still correct to regain balance, can be rotated and skewed so as to model the effects of a repetitive training of a rotational movement pattern. For the purposes of our study a rotation and a skew matrix is applied to the critical curve of the model. We present here a linear stability analysis of the modified model, as well as a fit of the model to experimental data of two characteristic "asymmetric" elite athletes and to a "symmetric" elite athlete for comparison. The new adapted model has many uses not just in sport but also in rehabilitation, as many work place injuries are caused by excessive repetition of unaligned and rotational movement patterns.

  7. Repetition-based Interactive Facade Modeling

    KAUST Repository

    AlHalawani, Sawsan

    2012-07-01

    Modeling and reconstruction of urban environments has gained researchers attention throughout the past few years. It spreads in a variety of directions across multiple disciplines such as image processing, computer graphics and computer vision as well as in architecture, geoscience and remote sensing. Having a virtual world of our real cities is very attractive in various directions such as entertainment, engineering, governments among many others. In this thesis, we address the problem of processing a single fa cade image to acquire useful information that can be utilized to manipulate the fa cade and generate variations of fa cade images which can be later used for buildings\\' texturing. Typical fa cade structures exhibit a rectilinear distribution where in windows and other elements are organized in a grid of horizontal and vertical repetitions of similar patterns. In the firt part of this thesis, we propose an efficient algorithm that exploits information obtained from a single image to identify the distribution grid of the dominant elements i.e. windows. This detection method is initially assisted with the user marking the dominant window followed by an automatic process for identifying its repeated instances which are used to define the structure grid. Given the distribution grid, we allow the user to interactively manipulate the fa cade by adding, deleting, resizing or repositioning the windows in order to generate new fa cade structures. Having the utility for the interactive fa cade is very valuable to create fa cade variations and generate new textures for building models. Ultimately, there is a wide range of interesting possibilities of interactions to be explored.

  8. Responses of Hodgkin-Huxley Neuronal Systems to Spike-Train Inputs

    Institute of Scientific and Technical Information of China (English)

    CHANG Wen-Li; WANG Sheng-Jun; WANG Ying-Hai

    2007-01-01

    We investigate responses of the Hodgkin-Huxley globally neuronal systems to periodic spike-train inputs. The firing activities of the neuronal networks show different rhythmic patterns for different parameters. These rhythmic patterns can be used to explain cycles of firing in real brain. These activity patterns, average activity and coherence measure are affected by two quantities such as the percentage of excitatory couplings and stimulus intensity, in which the percentage of excitatory couplings is more important than stimulus intensity since the transition phenomenon of average activity comes about.

  9. Encoding Chaos in Neural Spike Trains

    Science.gov (United States)

    Richardson, Kristen A.; Imhoff, Thomas T.; Grigg, Peter; Collins, James J.

    1998-03-01

    Recently, it has been shown that interspike interval (ISI) series from driven model neurons can be used to discriminate between chaotic and stochastic inputs. Here we extend this work to in vitro experimental studies with rat cutaneous mechanoreceptors. For each of the neurons tested, we show that a chaotically driven ISI series can be distinguished from a stochastically driven ISI series on the basis of a nonlinear prediction measure. This work demonstrates that dynamical information can be preserved when an analog chaotic signal is converted into a spike train by a sensory neuron.

  10. Solving constraint satisfaction problems with networks of spiking neurons

    Directory of Open Access Journals (Sweden)

    Zeno eJonke

    2016-03-01

    Full Text Available Network of neurons in the brain apply – unlike processors in our current generation ofcomputer hardware – an event-based processing strategy, where short pulses (spikes areemitted sparsely by neurons to signal the occurrence of an event at a particular point intime. Such spike-based computations promise to be substantially more power-efficient thantraditional clocked processing schemes. However it turned out to be surprisingly difficult todesign networks of spiking neurons that can solve difficult computational problems on the levelof single spikes (rather than rates of spikes. We present here a new method for designingnetworks of spiking neurons via an energy function. Furthermore we show how the energyfunction of a network of stochastically firing neurons can be shaped in a quite transparentmanner by composing the networks of simple stereotypical network motifs. We show that thisdesign approach enables networks of spiking neurons to produce approximate solutions todifficult (NP-hard constraint satisfaction problems from the domains of planning/optimizationand verification/logical inference. The resulting networks employ noise as a computationalresource. Nevertheless the timing of spikes (rather than just spike rates plays an essential rolein their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines and Gibbs sampling.

  11. Consensus-Based Sorting of Neuronal Spike Waveforms

    Science.gov (United States)

    Fournier, Julien; Mueller, Christian M.; Shein-Idelson, Mark; Hemberger, Mike

    2016-01-01

    Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. PMID:27536990

  12. Dopamine-deprived striatal GABAergic interneurons burst and generate repetitive gigantic IPSCs in medium spiny neurons.

    Science.gov (United States)

    Dehorter, Nathalie; Guigoni, Celine; Lopez, Catherine; Hirsch, June; Eusebio, Alexandre; Ben-Ari, Yehezkel; Hammond, Constance

    2009-06-17

    Striatal GABAergic microcircuits modulate cortical responses and movement execution in part by controlling the activity of medium spiny neurons (MSNs). How this is altered by chronic dopamine depletion, such as in Parkinson's disease, is not presently understood. We now report that, in dopamine-depleted slices of the striatum, MSNs generate giant spontaneous postsynaptic GABAergic currents (single or in bursts at 60 Hz) interspersed with silent episodes, rather than the continuous, low-frequency GABAergic drive (5 Hz) observed in control MSNs. This shift was observed in one-half of the MSN population, including both "D(1)-negative" and "D(1)-positive" MSNs. Single GABA and NMDA channel recordings revealed that the resting membrane potential and reversal potential of GABA were similar in control and dopamine-depleted MSNs, and depolarizing, but not excitatory, actions of GABA were observed. Glutamatergic and cholinergic antagonists did not block the GABAergic oscillations, suggesting that they were generated by GABAergic neurons. In support of this, cell-attached recordings revealed that a subpopulation of intrastriatal GABAergic interneurons generated bursts of spikes in dopamine-deprived conditions. This subpopulation included low-threshold spike interneurons but not fast-spiking interneurons, cholinergic interneurons, or MSNs. Therefore, a population of local GABAergic interneurons shifts from tonic to oscillatory mode when dopamine deprived and gives rise to spontaneous repetitive giant GABAergic currents in one-half the MSNs. We suggest that this may in turn alter integration of cortical signals by MSNs.

  13. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

    Science.gov (United States)

    Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

    2017-10-01

    Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

  14. Decoding of the spike timing of primary afferents during voluntary arm movements in monkeys

    Directory of Open Access Journals (Sweden)

    Tatsuya eUmeda

    2014-05-01

    Full Text Available Understanding the mechanisms of encoding forelimb kinematics in the activity of peripheral afferents is essential for determining the optimal parameters of afferent stimulation to transmit proprioceptive signals in neuroprosthetics. To investigate whether the spike timing of dorsal root ganglion (DRG neurons could be estimated from the forelimb kinematics of behaving monkeys, we implanted two multi-electrode arrays chronically in the DRGs at the level of the cervical segments in two monkeys. Neuronal activity during voluntary reach-to-grasp movements were recorded simultaneously with the trajectories of hand/arm movements, which were tracked in three-dimensional space using a motion capture system. Sixteen and 13 neurons, including muscle spindles, skin receptors, and tendon organ afferents, were recorded in the two monkeys, respectively. We were able to reconstruct forelimb joint kinematics from the temporal firing pattern of a subset of DRG neurons using sparse linear regression (SLiR analysis, suggesting that DRG neuronal ensembles encoded information about joint kinematics. Furthermore, we estimated the spike timing of the DRG neuronal ensembles from joint kinematics using an integrate-and-fire model (IF incorporating the SLiR algorithm. The temporal change of firing frequency of a subpopulation of neurons was reconstructed precisely from forelimb kinematics using the SLiR. The spike timing of the DRG neurons was calculated using an IF model, in which a spike occurs if the cumulative sum of the firing frequency value exceeded a constant threshold. The estimated firing pattern of the DRG neuronal ensembles encoded forelimb joint angles and velocities as precisely as the originally recorded neuronal activity. These results suggest that the simple model can be used to generate an accurate estimate of the spike timing of DRG neuronal ensembles from forelimb joint kinematics, and is useful for designing a proprioceptive decoder in a brain machine

  15. Limits of spiked random matrices I

    CERN Document Server

    Bloemendal, Alex

    2010-01-01

    Given a large, high-dimensional sample from a spiked population, the top sample covariance eigenvalue is known to exhibit a phase transition. We show that the largest eigenvalues have asymptotic distributions near the phase transition in the rank-one spiked real Wishart setting and its general beta analogue, proving a conjecture of Baik, Ben Arous and P\\'ech\\'e. We also treat shifted mean Gaussian orthogonal and beta ensembles. Such results are entirely new in the real case; in the complex case we strengthen existing results by providing optimal scaling assumptions. One obtains the known limiting random Schr\\"odinger operator on the half-line, but the boundary condition now depends on the perturbation. We derive several characterizations of the limit laws in which beta appears as a parameter, including a simple linear boundary value problem. This PDE description recovers known explicit formulas at beta=2,4, yielding in particular a new and simple proof of the Painlev\\'e representations for these Tracy-Widom d...

  16. Phase Diagram of Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamed eSeyed-Allaei

    2015-03-01

    Full Text Available In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probablilty of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations. but here, I take a different perspective, inspired by evolution. I simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable by nature. Networks which are configured according to the common values, have the best dynamic range in response to an impulse and their dynamic range is more robust in respect to synaptic weights. In fact, evolution has favored networks of best dynamic range. I present a phase diagram that shows the dynamic ranges of different networks of different parameteres. This phase diagram gives an insight into the space of parameters -- excitatory to inhibitory ratio, sparseness of connections and synaptic weights. It may serve as a guideline to decide about the values of parameters in a simulation of spiking neural network.

  17. Communication through resonance in spiking neuronal networks.

    Science.gov (United States)

    Hahn, Gerald; Bujan, Alejandro F; Frégnac, Yves; Aertsen, Ad; Kumar, Arvind

    2014-08-01

    The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance"). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks.

  18. The effects of spike frequency adaptation and negative feedback on the synchronization of neural oscillators.

    Science.gov (United States)

    Ermentrout, B; Pascal, M; Gutkin, B

    2001-06-01

    There are several different biophysical mechanisms for spike frequency adaptation observed in recordings from cortical neurons. The two most commonly used in modeling studies are a calcium-dependent potassium current I(ahp) and a slow voltage-dependent potassium current, I(m). We show that both of these have strong effects on the synchronization properties of excitatorily coupled neurons. Furthermore, we show that the reasons for these effects are different. We show through an analysis of some standard models, that the M-current adaptation alters the mechanism for repetitive firing, while the afterhyperpolarization adaptation works via shunting the incoming synapses. This latter mechanism applies with a network that has recurrent inhibition. The shunting behavior is captured in a simple two-variable reduced model that arises near certain types of bifurcations. A one-dimensional map is derived from the simplified model.

  19. Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

    Directory of Open Access Journals (Sweden)

    Daniel ede Santos-Sierra

    2015-11-01

    Full Text Available Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions cite{Pyragas}, where the slave neuron is able to anticipate in time the behaviour of the master one. In this paper we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI, one of the main features of the neural response associated to the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

  20. An Overview of Bayesian Methods for Neural Spike Train Analysis

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  1. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

  2. Geomagnetic spikes on the core-mantle boundary

    Science.gov (United States)

    Davies, Christopher; Constable, Catherine

    2017-05-01

    Extreme variations of Earth's magnetic field occurred in the Levant region around 1000 BC, when the field intensity rapidly rose and fell by a factor of 2. No coherent link currently exists between this intensity spike and the global field produced by the core geodynamo. Here we show that the Levantine spike must span >60° longitude at Earth's surface if it originates from the core-mantle boundary (CMB). Several low intensity data are incompatible with this geometric bound, though age uncertainties suggest these data could have sampled the field before the spike emerged. Models that best satisfy energetic and geometric constraints produce CMB spikes 8-22° wide, peaking at O(100) mT. We suggest that the Levantine spike reflects an intense CMB flux patch that grew in place before migrating northwest, contributing to growth of the dipole field. Estimates of Ohmic heating suggest that diffusive processes likely govern the ultimate decay of geomagnetic spikes.

  3. Cytoplasmic tail of coronavirus spike protein has intracellular targeting signals

    Indian Academy of Sciences (India)

    JIBIN SADASIVAN; MANMEET SINGH; JAYASRI DAS SARMA

    2017-06-01

    Intracellular trafficking and localization studies of spike protein from SARS and OC43 showed that SARS spikeprotein is localized in the ER or ERGIC compartment and OC43 spike protein is predominantly localized in thelysosome. Differential localization can be explained by signal sequence. The sequence alignment using Clustal Wshows that the signal sequence present at the cytoplasmic tail plays an important role in spike protein localization. Aunique GYQEL motif is identified at the cytoplasmic terminal of OC43 spike protein which helps in localization in thelysosome, and a novel KLHYT motif is identified in the cytoplasmic tail of SARS spike protein which helps in ER orERGIC localization. This study sheds some light on the role of cytoplasmic tail of spike protein in cell-to-cell fusion,coronavirus host cell fusion and subsequent pathogenicity.

  4. Stochastic optimal control of single neuron spike trains

    DEFF Research Database (Denmark)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

    2014-01-01

    Objective. External control of spike times in single neurons can reveal important information about a neuron's sub-threshold dynamics that lead to spiking, and has the potential to improve brain–machine interfaces and neural prostheses. The goal of this paper is the design of optimal electrical...... stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...

  5. The local field potential reflects surplus spike synchrony

    DEFF Research Database (Denmark)

    Denker, Michael; Roux, Sébastien; Lindén, Henrik;

    2011-01-01

    While oscillations of the local field potential (LFP) are commonly attributed to the synchronization of neuronal firing rate on the same time scale, their relationship to coincident spiking in the millisecond range is unknown. Here, we present experimental evidence to reconcile the notions...... of synchrony at the level of spiking and at the mesoscopic scale. We demonstrate that only in time intervals of significant spike synchrony that cannot be explained on the basis of firing rates, coincident spikes are better phase locked to the LFP than predicted by the locking of the individual spikes....... This effect is enhanced in periods of large LFP amplitudes. A quantitative model explains the LFP dynamics by the orchestrated spiking activity in neuronal groups that contribute the observed surplus synchrony. From the correlation analysis, we infer that neurons participate in different constellations...

  6. A spiking neuron circuit based on a carbon nanotube transistor.

    Science.gov (United States)

    Chen, C-L; Kim, K; Truong, Q; Shen, A; Li, Z; Chen, Y

    2012-07-11

    A spiking neuron circuit based on a carbon nanotube (CNT) transistor is presented in this paper. The spiking neuron circuit has a crossbar architecture in which the transistor gates are connected to its row electrodes and the transistor sources are connected to its column electrodes. An electrochemical cell is incorporated in the gate of the transistor by sandwiching a hydrogen-doped poly(ethylene glycol)methyl ether (PEG) electrolyte between the CNT channel and the top gate electrode. An input spike applied to the gate triggers a dynamic drift of the hydrogen ions in the PEG electrolyte, resulting in a post-synaptic current (PSC) through the CNT channel. Spikes input into the rows trigger PSCs through multiple CNT transistors, and PSCs cumulate in the columns and integrate into a 'soma' circuit to trigger output spikes based on an integrate-and-fire mechanism. The spiking neuron circuit can potentially emulate biological neuron networks and their intelligent functions.

  7. The Prevalence and Phenomenology of Repetitive Behavior in Genetic Syndromes

    Science.gov (United States)

    Moss, Joanna; Oliver, Chris; Arron, Kate; Burbidge, Cheryl; Berg, Katy

    2009-01-01

    We investigated the prevalence and phenomenology of repetitive behavior in genetic syndromes to detail profiles of behavior. The Repetitive Behaviour Questionnaire (RBQ) provides fine-grained identification of repetitive behaviors. The RBQ was employed to examine repetitive behavior in Angelman (N = 104), Cornelia de Lange (N = 101), Cri-du-Chat…

  8. Higher-order correlations in common input shapes the output spiking activity of a neural population

    Science.gov (United States)

    Montangie, Lisandro; Montani, Fernando

    2017-04-01

    Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from q-Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter q. We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.

  9. Estimating nonstationary input signals from a single neuronal spike train

    OpenAIRE

    Kim, Hideaki; Shinomoto, Shigeru

    2012-01-01

    Neurons temporally integrate input signals, translating them into timed output spikes. Because neurons nonperiodically emit spikes, examining spike timing can reveal information about input signals, which are determined by activities in the populations of excitatory and inhibitory presynaptic neurons. Although a number of mathematical methods have been developed to estimate such input parameters as the mean and fluctuation of the input current, these techniques are based on the unrealistic as...

  10. The Role of Spike Temporal Latencies in Artificial Olfaction

    Science.gov (United States)

    Polese, D.; Martinelli, E.; Dini, F.; Paolesse, R.; Filippini, D.; Lundström, I.; Di Natale, C.

    2011-09-01

    In this paper we investigate the recognition power of spike time latencies in an artificial olfactory system. For the scope we used a recently introduced platform for artificial olfaction implementing an artificial olfactory epithelium, formed by thousands sensors, and an abstract olfactory bulb1. Results show that correct volatile compounds classification can be achieved considering only the first two spikes of the neural network output evidencing that the latency of the first spikes contains actually enough information for odor identification.

  11. Likelihood methods and classical burster repetition

    CERN Document Server

    Graziani, C; Graziani, Carlo; Lamb, Donald Q

    1995-01-01

    We develop a likelihood methodology which can be used to search for evidence of burst repetition in the BATSE catalog, and to study the properties of the repetition signal. We use a simplified model of burst repetition in which a number N_{\\rm r} of sources which repeat a fixed number of times N_{\\rm rep} are superposed upon a number N_{\\rm nr} of non-repeating sources. The instrument exposure is explicitly taken into account. By computing the likelihood for the data, we construct a probability distribution in parameter space that may be used to infer the probability that a repetition signal is present, and to estimate the values of the repetition parameters. The likelihood function contains contributions from all the bursts, irrespective of the size of their positional errors --- the more uncertain a burst's position is, the less constraining is its contribution. Thus this approach makes maximal use of the data, and avoids the ambiguities of sample selection associated with data cuts on error circle size. We...

  12. Temporal properties of network-mediated responses to repetitive stimuli are dependent upon retinal ganglion cell type

    Science.gov (United States)

    Im, Maesoon; Fried, Shelley I.

    2016-04-01

    Objective. To provide artificially-elicited vision that is temporally dynamic, retinal prosthetic devices will need to repeatedly stimulate retinal neurons. However, given the diversity of physiological types of retinal ganglion cells (RGCs) as well as the heterogeneity of their responses to electric stimulation, temporal properties of RGC responses have not been adequately investigated. Here, we explored the cell type dependence of network-mediated RGC responses to repetitive electric stimulation at various stimulation rates. Approach. We examined responses of ON and OFF types of RGCs in the rabbit retinal explant to five consecutive stimuli with varying inter-stimulus intervals (10-1000 ms). Each stimulus was a 4 ms long monophasic sinusoidal cathodal current, which was applied epiretinally via a conical electrode. Spiking activity of targeted RGCs was recorded using a cell-attached patch electrode. Main results. ON and OFF cells had distinct responses to repetitive stimuli. Consistent with earlier studies, OFF cells always generated reduced responses to subsequent stimuli compared to responses to the first stimulus. In contrast, a new stimulus to ON cells suppressed all pending/ongoing responses from previous stimuli and initiated its own response that was remarkably similar to the response from a single stimulus in isolation. This previously unreported ‘reset’ behavior was observed exclusively and consistently in ON cells. These contrasts between ON and OFF cells created a range of stimulation rates (4-7 Hz) that maximized the ratio of the responses arising in ON versus OFF cells. Significance. Previous clinical testing reported that subjects perceive bright phosphenes (ON responses) and also prefer stimulation rates of 5-7 Hz. Our results suggest that responses of ON cells are weak at high rates of stimulation (> ˜7 Hz) due to the reset while responses of OFF cells are strong at low rates (cells more closely match physiological patterns (Im and Fried 2015

  13. Spike Inference from Calcium Imaging using Sequential Monte Carlo Methods

    OpenAIRE

    NeuroData; Paninski, L

    2015-01-01

    Vogelstein JT, Paninski L. Spike Inference from Calcium Imaging using Sequential Monte Carlo Methods. Statistical and Applied Mathematical Sciences Institute (SAMSI) Program on Sequential Monte Carlo Methods, 2008

  14. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.

    Science.gov (United States)

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.

  15. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity.

    Science.gov (United States)

    Indiveri, Giacomo; Chicca, Elisabetta; Douglas, Rodney

    2006-01-01

    We present a mixed-mode analog/digital VLSI device comprising an array of leaky integrate-and-fire (I&F) neurons, adaptive synapses with spike-timing dependent plasticity, and an asynchronous event based communication infrastructure that allows the user to (re)configure networks of spiking neurons with arbitrary topologies. The asynchronous communication protocol used by the silicon neurons to transmit spikes (events) off-chip and the silicon synapses to receive spikes from the outside is based on the "address-event representation" (AER). We describe the analog circuits designed to implement the silicon neurons and synapses and present experimental data showing the neuron's response properties and the synapses characteristics, in response to AER input spike trains. Our results indicate that these circuits can be used in massively parallel VLSI networks of I&F neurons to simulate real-time complex spike-based learning algorithms.

  16. Self-control with spiking and non-spiking neural networks playing games.

    Science.gov (United States)

    Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

    2010-01-01

    Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the

  17. Presynaptic spike broadening reduces junctional potential amplitude.

    Science.gov (United States)

    Spencer, A N; Przysiezniak, J; Acosta-Urquidi, J; Basarsky, T A

    1989-08-24

    Presynaptic modulation of action potential duration may regulate synaptic transmission in both vertebrates and invertebrates. Such synaptic plasticity is brought about by modifications to membrane currents at presynaptic release sites, which, in turn, lead to changes in the concentration of cytosolic calcium available for mediating transmitter release. The 'primitive' neuromuscular junction of the jellyfish Polyorchis penicillatus is a useful model of presynaptic modulation. In this study, we show that the durations of action potentials in the motor neurons of this jellyfish are negatively correlated with the amplitude of excitatory junctional potentials. We present data from in vitro voltage-clamp experiments showing that short duration voltage spikes, which elicit large excitatory junctional potentials in vivo, produce larger and briefer calcium currents than do long duration action potentials, which elicit small excitatory junctional potentials.

  18. A Spiking Neural Learning Classifier System

    CERN Document Server

    Howard, Gerard; Lanzi, Pier-Luca

    2012-01-01

    Learning Classifier Systems (LCS) are population-based reinforcement learners used in a wide variety of applications. This paper presents a LCS where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. We employ a constructivist model of growth of both neurons and dendrites that realise flexible learning by evolving structures of sufficient complexity to solve a well-known problem involving continuous, real-valued inputs. Additionally, we extend the system to enable temporal state decomposition. By allowing our LCS to chain together sequences of heterogeneous actions into macro-actions, it is shown to perform optimally in a problem where traditional methods can fail to find a solution in a reasonable amount of time. Our final system is tested on a simulated robotics platform.

  19. [Wide QRS tachycardia preceded by pacemaker spikes].

    Science.gov (United States)

    Romero, M; Aranda, A; Gómez, F J; Jurado, A

    2014-04-01

    The differential diagnosis and therapeutic management of wide QRS tachycardia preceded by pacemaker spike is presented. The pacemaker-mediated tachycardia, tachycardia fibrillo-flutter in patients with pacemakers, and runaway pacemakers, have a similar surface electrocardiogram, but respond to different therapeutic measures. The tachycardia response to the application of a magnet over the pacemaker could help in the differential diagnosis, and in some cases will be therapeutic, as in the case of a tachycardia-mediated pacemaker. Although these conditions are diagnosed and treated in hospitals with catheterization laboratories using the application programmer over the pacemaker, patients presenting in primary care clinic and emergency forced us to make a diagnosis and treat the haemodynamically unstable patient prior to referral. Copyright © 2012 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España. All rights reserved.

  20. A novel spatiotemporal analysis of peri-ictal spiking to probe the relation of spikes and seizures in epilepsy.

    Science.gov (United States)

    Krishnan, Balu; Vlachos, Ioannis; Faith, Aaron; Mullane, Steven; Williams, Korwyn; Alexopoulos, Andreas; Iasemidis, Leonidas

    2014-08-01

    The relation between epileptic spikes and seizures is an important but still unresolved question in epilepsy research. Preclinical and clinical studies have produced inconclusive results on the causality or even on the existence of such a relation. We set to investigate this relation taking in consideration seizure severity and spatial extent of spike rate. We developed a novel automated spike detection algorithm based on morphological filtering techniques and then tested the hypothesis that there is a pre-ictal increase and post-ictal decrease of the spatial extent of spike rate. Peri-ictal (around seizures) spikes were detected from intracranial EEG recordings in 5 patients with temporal lobe epilepsy. The 94 recorded seizures were classified into two classes, based on the percentage of brain sites having higher or lower rate of spikes in the pre-ictal compared to post-ictal periods, with a classification accuracy of 87.4%. This seizure classification showed that seizures with increased pre-ictal spike rate and spatial extent compared to the post-ictal period were mostly (83%) clinical seizures, whereas no such statistically significant (α = 0.05) increase was observed peri-ictally in 93% of sub-clinical seizures. These consistent across patients results show the existence of a causal relation between spikes and clinical seizures, and imply resetting of the preceding spiking process by clinical seizures.

  1. Cognitive Set Shifting Deficits and Their Relationship to Repetitive Behaviors in Autism Spectrum Disorder

    Science.gov (United States)

    Miller, Haylie L.; Ragozzino, Michael E.; Cook, Edwin H.; Sweeney, John A.; Mosconi, Matthew W.

    2015-01-01

    The neurocognitive impairments associated with restricted and repetitive behaviors (RRBs) in autism spectrum disorder (ASD) are not yet clear. Prior studies indicate that individuals with ASD show reduced cognitive flexibility, which could reflect difficulty shifting from a previously learned response pattern or a failure to maintain a new…

  2. Protein histochemistry using coronaviral spike proteins: studying binding profiles and sialic acid requirements for attachment to tissues

    NARCIS (Netherlands)

    Ambepitiya Wickramasinghe, I.N.; Verheije, M.H.

    2015-01-01

    Protein histochemistry is a tissue-based technique that enables the analysis of viral attachment patterns as well as the identification of specific viral and host determinants involved in the first step in the infection of a host cell by a virus. Applying recombinantly expressed spike proteins of in

  3. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  4. Interspecific "common" repetitive DNA sequences in salamanders of the genus Plethodon.

    Science.gov (United States)

    Mizuno, S; Andrews, C; Macgregor, H C

    1976-10-12

    Intermediate repetitive sequences of Plethodon cinereus which comprised about 30% of the genomic DNA were isolated and iodinated with 125I. About 5% of the 125I-repetitive fraction hybridized with a large excess of DNA from P. dunni at Cot 20. About half of the 125I-DNA in the hybrids was resistant to extensive digestion with S-1 nuclease. The average molecular size of the S-1 nuclease-resistant fraction was about 100 nucleotide pairs. The melting temperature of the S-1 nuclease-resistant fraction was about 2 degrees lower than that of the corresponding fraction made with P. cinereus DNA. These results are taken to indicate the presence in the genomes of P. cinereus and P. dunni of evolutionarily stable "common" repetitive sequences. The average frequency of repetition of the common repetitive sequences is about 6,000 X in both species. The common repetitive fraction is also present in the genomes of other species of Plethodon, although the general populations of intermediate repetitive sequences are markedly different from one species to another. The cinereus--dunni common repetitive sequences could not be detected in plethodontids belonging to different tribes, nor in more distantly related amphibians. The profiles of binding of the common repetitive sequences to CsCl or CS2SO4-Ag+ density gradient fractions of P. dunni DNA suggested that these sequences consisted of heterogeneous components with respect to base compositions, and that they did not include large amounts of the genes for ribosomal RNA, 5S RNA, 4S RNA, or histone messenger RNA. In situ hybridization of the 3H-labelled intermediate repetitive sequences of P. cinereus to male meiotic chromosomes of the same species gave autoradiographs after an exposure of seven days showing all 14 chromosomes labelled. The pattern of labelling appeared not to be random, but was impossible to analyse on account of the irregular shapes and different degrees of stretching of diplotene and prometaphase chromosomes. In

  5. Neural dynamics during repetitive visual stimulation

    Science.gov (United States)

    Tsoneva, Tsvetomira; Garcia-Molina, Gary; Desain, Peter

    2015-12-01

    Objective. Steady-state visual evoked potentials (SSVEPs), the brain responses to repetitive visual stimulation (RVS), are widely utilized in neuroscience. Their high signal-to-noise ratio and ability to entrain oscillatory brain activity are beneficial for their applications in brain-computer interfaces, investigation of neural processes underlying brain rhythmic activity (steady-state topography) and probing the causal role of brain rhythms in cognition and emotion. This paper aims at analyzing the space and time EEG dynamics in response to RVS at the frequency of stimulation and ongoing rhythms in the delta, theta, alpha, beta, and gamma bands. Approach.We used electroencephalography (EEG) to study the oscillatory brain dynamics during RVS at 10 frequencies in the gamma band (40-60 Hz). We collected an extensive EEG data set from 32 participants and analyzed the RVS evoked and induced responses in the time-frequency domain. Main results. Stable SSVEP over parieto-occipital sites was observed at each of the fundamental frequencies and their harmonics and sub-harmonics. Both the strength and the spatial propagation of the SSVEP response seem sensitive to stimulus frequency. The SSVEP was more localized around the parieto-occipital sites for higher frequencies (>54 Hz) and spread to fronto-central locations for lower frequencies. We observed a strong negative correlation between stimulation frequency and relative power change at that frequency, the first harmonic and the sub-harmonic components over occipital sites. Interestingly, over parietal sites for sub-harmonics a positive correlation of relative power change and stimulation frequency was found. A number of distinct patterns in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz) and beta (15-30 Hz) bands were also observed. The transient response, from 0 to about 300 ms after stimulation onset, was accompanied by increase in delta and theta power over fronto-central and occipital sites, which returned to baseline

  6. Regulation of spike timing-dependent plasticity of olfactory inputs in mitral cells in the rat olfactory bulb.

    Directory of Open Access Journals (Sweden)

    Teng-Fei Ma

    Full Text Available The recent history of activity input onto granule cells (GCs in the main olfactory bulb can affect the strength of lateral inhibition, which functions to generate contrast enhancement. However, at the plasticity level, it is unknown whether and how the prior modification of lateral inhibition modulates the subsequent induction of long-lasting changes of the excitatory olfactory nerve (ON inputs to mitral cells (MCs. Here we found that the repetitive stimulation of two distinct excitatory inputs to the GCs induced a persistent modification of lateral inhibition in MCs in opposing directions. This bidirectional modification of inhibitory inputs differentially regulated the subsequent synaptic plasticity of the excitatory ON inputs to the MCs, which was induced by the repetitive pairing of excitatory postsynaptic potentials (EPSPs with postsynaptic bursts. The regulation of spike timing-dependent plasticity (STDP was achieved by the regulation of the inter-spike-interval (ISI of the postsynaptic bursts. This novel form of inhibition-dependent regulation of plasticity may contribute to the encoding or processing of olfactory information in the olfactory bulb.

  7. Spike-timing computation properties of a feed-forward neural network model

    Directory of Open Access Journals (Sweden)

    Drew Benjamin Sinha

    2014-01-01

    Full Text Available Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g. serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

  8. Spike timing dependent plasticity: a consequence of more fundamental learning rules

    Directory of Open Access Journals (Sweden)

    Harel Z Shouval

    2010-07-01

    Full Text Available Spike timing dependent plasticity (STDP is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse - the “first law” of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse’s susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

  9. Parvalbumin tunes spike-timing and efferent short-term plasticity in striatal fast spiking interneurons.

    Science.gov (United States)

    Orduz, David; Bischop, Don Patrick; Schwaller, Beat; Schiffmann, Serge N; Gall, David

    2013-07-01

      Striatal fast spiking interneurons (FSIs) modulate output of the striatum by synchronizing medium-sized spiny neurons (MSNs). Recent studies have broadened our understanding of FSIs, showing that they are implicated in severe motor disorders such as parkinsonism, dystonia and Tourette syndrome. FSIs are the only striatal neurons to express the calcium-binding protein parvalbumin (PV). This selective expression of PV raises questions about the functional role of this Ca(2+) buffer in controlling FSI Ca(2+) dynamics and, consequently, FSI spiking mode and neurotransmission. To study the functional involvement of FSIs in striatal microcircuit activity and the role of PV in FSI function, we performed perforated patch recordings on enhanced green fluorescent protein-expressing FSIs in brain slices from control and PV-/- mice. Our results revealed that PV-/- FSIs fired more regularly and were more excitable than control FSIs by a mechanism in which Ca(2+) buffering is linked to spiking activity as a result of the activation of small conductance Ca(2+)-dependent K(+) channels. A modelling approach of striatal FSIs supports our experimental results. Furthermore, PV deletion modified frequency-specific short-term plasticity at inhibitory FSI to MSN synapses. Our results therefore reinforce the hypothesis that in FSIs, PV is crucial for fine-tuning of the temporal responses of the FSI network and for the orchestration of MSN populations. This, in turn, may play a direct role in the generation and pathology-related worsening of motor rhythms.

  10. Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms

    Science.gov (United States)

    Matthews, Brett A.; Clements, Mark A.

    2014-01-01

    This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty (i.e., with additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98% reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04% reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset. PMID:24829568

  11. Reliability of spike and burst firing in thalamocortical relay cells

    NARCIS (Netherlands)

    Zeldenrust, F.; Chameau, P.J.P.; Wadman, W.J.

    2013-01-01

    The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice and also in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimpose

  12. A new class of metrics for spike trains.

    Science.gov (United States)

    Rusu, Cătălin V; Florian, Răzvan V

    2014-02-01

    The distance between a pair of spike trains, quantifying the differences between them, can be measured using various metrics. Here we introduce a new class of spike train metrics, inspired by the Pompeiu-Hausdorff distance, and compare them with existing metrics. Some of our new metrics (the modulus-metric and the max-metric) have characteristics that are qualitatively different from those of classical metrics like the van Rossum distance or the Victor and Purpura distance. The modulus-metric and the max-metric are particularly suitable for measuring distances between spike trains where information is encoded in bursts, but the number and the timing of spikes inside a burst do not carry information. The modulus-metric does not depend on any parameters and can be computed using a fast algorithm whose time depends linearly on the number of spikes in the two spike trains. We also introduce localized versions of the new metrics, which could have the biologically relevant interpretation of measuring the differences between spike trains as they are perceived at a particular moment in time by a neuron receiving these spike trains.

  13. No WIMP mini-spikes in dwarf spheroidal galaxies

    NARCIS (Netherlands)

    Wanders, M.; Bertone, G.; Volonteri, M.; Weniger, C.

    2015-01-01

    The formation of black holes inevitably affects the distribution of dark and baryonic matter in their vicinity, leading to an enhancement of the dark matter density, called spike, and if dark matter is made of WIMPs, to a strong enhancement of the dark matter annihilation rate. Spikes at the center

  14. Spike wave location and density disturb sleep slow waves in patients with CSWS (continuous spike waves during sleep).

    Science.gov (United States)

    Bölsterli Heinzle, Bigna K; Fattinger, Sara; Kurth, Salomé; Lebourgeois, Monique K; Ringli, Maya; Bast, Thomas; Critelli, Hanne; Schmitt, Bernhard; Huber, Reto

    2014-04-01

    In CSWS (continuous spike waves during sleep) activation of spike waves during slow wave sleep has been causally linked to neuropsychological deficits, but the pathophysiologic mechanisms are still unknown. In healthy subjects, the overnight decrease of the slope of slow waves in NREM (non-rapid eye movement) sleep has been linked to brain recovery to regain optimal cognitive performance. Here, we investigated whether the electrophysiologic hallmark of CSWS, the spike waves during sleep, is related to an alteration in the overnight decrease of the slope, and if this alteration is linked to location and density of spike waves. In a retrospective study, the slope of slow waves (0.5-2 Hz) in the first hour and last hour of sleep (19 electroencephalography [EEG] electrodes) of 14 patients with CSWS (3.1-13.5 years) was calculated. The spike wave "focus" was determined as the location of highest spike amplitude and the density of spike waves as spike wave index (SWI). There was no overnight change of the slope of slow waves in the "focus." Instead, in "nonfocal" regions, the slope decreased significantly. This difference in the overnight course resulted in a steeper slope in the "focus" compared to "nonfocal" electrodes during the last hour of sleep. Spike wave density was correlated with the impairment of the overnight slope decrease: The higher the SWI, the more hampered the slope decrease. Location and density of spike waves are related to an alteration of the physiologic overnight decrease of the slow wave slope. This overnight decrease of the slope was shown to be closely related to the recovery function of sleep. Such recovery is necessary for optimal cognitive performance during wakefulness. Therefore we propose the impairment of this process by spike waves as a potential mechanism leading to neuropsychological deficits in CSWS. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here. Wiley Periodicals

  15. The neurobiology of repetitive behavior : of mice…

    NARCIS (Netherlands)

    Langen, Marieke; Kas, Martien J H; Staal, Wouter G; van Engeland, Herman; Durston, Sarah

    2011-01-01

    Repetitive and stereotyped behavior is a prominent element of both animal and human behavior. Similar behavior is seen across species, in diverse neuropsychiatric disorders and in key phases of typical development. This raises the question whether these similar classes of behavior are caused by simi

  16. Large-scale detection of repetitions.

    Science.gov (United States)

    Smyth, W F

    2014-05-28

    Combinatorics on words began more than a century ago with a demonstration that an infinitely long string with no repetitions could be constructed on an alphabet of only three letters. Computing all the repetitions (such as ∙∙∙TTT ∙∙∙ or ∙∙∙ CGACGA ∙∙∙ ) in a given string x of length n is one of the oldest and most important problems of computational stringology, requiring time in the worst case. About a dozen years ago, it was discovered that repetitions can be computed as a by-product of the Θ(n)-time computation of all the maximal periodicities or runs in x. However, even though the computation is linear, it is also brute force: global data structures, such as the suffix array, the longest common prefix array and the Lempel-Ziv factorization, need to be computed in a preprocessing phase. Furthermore, all of this effort is required despite the fact that the expected number of runs in a string is generally a small fraction of the string length. In this paper, I explore the possibility that repetitions (perhaps also other regularities in strings) can be computed in a manner commensurate with the size of the output.

  17. Verbal Repetitions and Echolalia in Alzheimer's Discourse

    Science.gov (United States)

    Da Cruz, Fernanda Miranda

    2010-01-01

    This article reports on an investigation of echolalic repetition in Alzheimer's disease (AD). A qualitative analysis of data from spontaneous conversations with MHI, a woman with AD, is presented. The data come from the DALI Corpus, a corpus of spontaneous conversations involving subjects with AD. This study argues that echolalic effects can be…

  18. Neurobehavioural Correlates of Abnormal Repetitive Behaviour

    Directory of Open Access Journals (Sweden)

    R. A. Ford

    1991-01-01

    Full Text Available Conditions in which echolalia and echopraxia occur are reviewed, followed by an attempt to elicit possible mechanisms of these phenomena. A brief description of stereotypical and perseverative behaviour and obsessional phenomena is given. It is suggested that abnormal repetitive behaviour may occur partly as a result of central dopaminergic dysfunction.

  19. Reducing Repetitive Speech: Effects of Strategy Instruction.

    Science.gov (United States)

    Dipipi, Caroline M.; Jitendra, Asha K.; Miller, Judith A.

    2001-01-01

    This article describes an intervention with an 18-year-old young woman with mild mental retardation and a seizure disorder, which focused on her repetitive echolalic verbalizations. The intervention included time delay, differential reinforcement of other behaviors, and self-monitoring. Overall, the intervention was successful in facilitating…

  20. Verbal Repetitions and Echolalia in Alzheimer's Discourse

    Science.gov (United States)

    Da Cruz, Fernanda Miranda

    2010-01-01

    This article reports on an investigation of echolalic repetition in Alzheimer's disease (AD). A qualitative analysis of data from spontaneous conversations with MHI, a woman with AD, is presented. The data come from the DALI Corpus, a corpus of spontaneous conversations involving subjects with AD. This study argues that echolalic effects can be…

  1. Numerical study of spike characteristics due to the motions of a non-spherical rebounding bubble

    Institute of Scientific and Technical Information of China (English)

    王加夏; 宗智; 孙雷; 李章锐; 姜明佐

    2016-01-01

    The boundary integral method (BIM) is used to simulate the 3-D gas bubble, generated within the two bubble pulsation periods in proximity to a free surface in an inviscid, incompressible and irrotational flow. The present method is well validated by comparing the calculated shapes of the bubble and the free surface with both the experimental results and the numerical ones obtained by the Axisymmetric BIM code. The expansion, the collapse of the gas bubble and the further evolution of the rebounding non-spherical bubble are simulated. The various variation patterns of the free surface spike and the bubble centroid for different standoff distances, the buoyancy parameters and the strength parameters are obtained to reveal the nonlinear interaction between the bubble and the free surface. The amplitude of the second maximum bubble volume and the four typical patterns of the bubble jet and the free surface spike are examined in the context of the standoff distance. The large buoyancy is used to elevate the spray dome rather than the free surface spike.

  2. A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing

    Directory of Open Access Journals (Sweden)

    William eLennon

    2014-12-01

    Full Text Available While the anatomy of the cerebellar microcircuit is well studied, how it implements cerebellar function is not understood. A number of models have been proposed to describe this mechanism but few emphasize the role of the vast network Purkinje cells (PKJs form with the molecular layer interneurons (MLIs – the stellate and basket cells. We propose a model of the MLI-PKJ network composed of simple spiking neurons incorporating the major anatomical and physiological features. In computer simulations, the model reproduces the irregular firing patterns observed in PKJs and MLIs in vitro and a shift toward faster, more regular firing patterns when inhibitory synaptic currents are blocked. In the model, the time between PKJ spikes is shown to be proportional to the amount of feedforward inhibition from an MLI on average. The two key elements of the model are: (1 spontaneously active PKJs and MLIs due to an endogenous depolarizing current, and (2 adherence to known anatomical connectivity along a parasagittal strip of cerebellar cortex. We propose this model to extend previous spiking network models of the cerebellum and for further computational investigation into the role of irregular firing and MLIs in cerebellar learning and function.

  3. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    Science.gov (United States)

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

  4. Reliability of SEMG spike parameters during concentric contractions.

    Science.gov (United States)

    Gabriel, D A

    2000-01-01

    This study examined the reliability of four surface electromyographic (SEMG) spike parameters during concentric (isotonic) contractions: mean spike amplitude, mean spike frequency, mean spike slope, and the mean number of peaks per spike. Eighteen subjects performed rapid elbow flexion on a horizontal angular displacement device that was used to measure joint torque. The SEMG activity of the biceps brachii was monitored with Beckman Ag/AgCl electrodes. The testing schedule consisted of four hundred trials distributed equally over four sessions. The stability of the means across sessions and the consistency of scores within subjects was determined for the first five (1-5) and last five (96-100) trials of each session to examine the possible influence of a "warm up" effect. All measures exhibited a significant (p contractions enhanced the ability to recruit more fast-twitch motor units across test days.

  5. Stochastic optimal control of single neuron spike trains

    DEFF Research Database (Denmark)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

    2014-01-01

    Objective. External control of spike times in single neurons can reveal important information about a neuron's sub-threshold dynamics that lead to spiking, and has the potential to improve brain–machine interfaces and neural prostheses. The goal of this paper is the design of optimal electrical...... stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... of control degrades with increasing intensity of the noise. Simulations show that our algorithms produce the desired results for the LIF model, but also for the case where the neuron dynamics are given by more complex models than the LIF model. This is illustrated explicitly using the Morris–Lecar spiking...

  6. Unsupervised Spike Sorting Based on Discriminative Subspace Learning

    CERN Document Server

    Keshtkaran, Mohammad Reza

    2014-01-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separab...

  7. Gamma Oscillations of Spiking Neural Populations Enhance Signal Discrimination

    Science.gov (United States)

    Masuda, Naoki; Doiron, Brent

    2007-01-01

    Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive. PMID:18052541

  8. Gamma oscillations of spiking neural populations enhance signal discrimination.

    Directory of Open Access Journals (Sweden)

    Naoki Masuda

    2007-11-01

    Full Text Available Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive.

  9. Property Analysis and Suppression Method of Real Measured Sea Spikes

    Directory of Open Access Journals (Sweden)

    Huang Yong

    2015-06-01

    Full Text Available In case of high-resolution, low grazing angle, high sea state, and horizontal transmitting, horizontal receiving polarization, the radar returns are strengthened, resulting in sea spikes. The sea spikes have the characteristics of high amplitudes, nonstationary, and non-Gaussian, which have a strong impact on the radar detection of weak marine moving targets. This study proposes a method for sea clutter suppression. Firstly, based on the sea spikes identification and selection method, the amplitude, temporal correlation, Doppler spectrum, and fractional power spectrum properties of sea spikes are analyzed. Secondly, the data to be detected are chosen by selecting the background clutter with minimum mean power, which can also eliminate the sea spikes. Correspondingly, sea clutter is suppressed with improved Signal-to-Clutter Ratio (SCR. Finally, the results of experiment with real radar data verify the effectiveness of the proposed method.

  10. Automated recognition of spikes in 1 Hz data recorded at the Easter Island magnetic observatory

    Science.gov (United States)

    Soloviev, Anatoly; Chulliat, Arnaud; Bogoutdinov, Shamil; Gvishiani, Alexei; Agayan, Sergey; Peltier, Aline; Heumez, Benoit

    2012-09-01

    In the present paper we apply a recently developed pattern recognition algorithm SPs to the problem of automated detection of artificial disturbances in one-second magnetic observatory data. The SPs algorithm relies on the theory of discrete mathematical analysis, which has been developed by some of the authors for more than 10 years. It continues the authors' research in the morphological analysis of time series using fuzzy logic techniques. We show that, after a learning phase, this algorithm is able to recognize artificial spikes uniformly with low probabilities of target miss and false alarm. In particular, a 94% spike recognition rate and a 6% false alarm rate were achieved as a result of the algorithm application to raw one-second data acquired at the Easter Island magnetic observatory. This capability is critical and opens the possibility to use the SPs algorithm in an operational environment.

  11. Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

    Science.gov (United States)

    Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

    2015-11-01

    The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.

  12. Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models.

    Science.gov (United States)

    Ba, Demba; Temereanca, Simona; Brown, Emery N

    2014-01-01

    Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) models cannot represent simultaneous occurrences of spike events at an arbitrarily small time resolution. Solo recently reported a simultaneous-event multivariate point process (SEMPP) model to correct this key limitation. In this paper, we show how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM). Unlike existing approximate procedures for fitting the discrete-time SEMPP model, the mGLM is an exact algorithm. The MPP time-rescaling theorem can be used to assess model goodness-of-fit. We also derive a new marked point-process (MkPP) representation of the SEMPP model that leads to new thinning and time-rescaling algorithms for simulating an SEMPP stochastic process. These algorithms are much simpler than multivariate extensions of algorithms for simulating a univariate point process, and could not be arrived at without the MkPP representation. We illustrate the versatility of the SEMPP model by analyzing neural spiking activity from pairs of simultaneously-recorded rat thalamic neurons stimulated by periodic whisker deflections, and by simulating SEMPP data. In the data analysis example, the SEMPP model demonstrates that whisker motion significantly modulates simultaneous spiking activity at the 1 ms time scale and that the stimulus effect is more than one order of magnitude greater for simultaneous activity compared with non-simultaneous activity. Together, the mGLM, the MPP time-rescaling theorem and the MkPP representation of the SEMPP model offer a theoretically sound, practical tool for measuring joint spiking propensity in a neuronal ensemble.

  13. Optimal control of laser plasma instabilities using Spike Trains of Uneven Duration and Delay (STUD pulses) for ICF and IFE

    CERN Document Server

    Afeyan, Bedros

    2012-01-01

    An adaptive method of controlling parametric instabilities in laser produced plasmas is proposed. It involves fast temporal modulation of a laser pulse on the fastest instability's amplification time scale, adapting to changing and unknown plasma conditions. These pulses are comprised of on and off sequences having at least one or two orders of magnitude contrast between them. Such laser illumination profiles are called STUD pulses for Spike Trains of Uneven Duration and Delay. The STUD pulse program includes scrambling the speckle patterns spatially in between the laser spikes. The off times allow damping of driven waves. The scrambling of the hot spots allows tens of damping times to elapse before hot spot locations experience recurring high intensity spikes. Damping in the meantime will have healed the scars of past growth. Another unique feature of STUD pulses on crossing beams is that their temporal profiles can be interlaced or staggered, and their interactions thus controlled with an on-off switch and ...

  14. Optimal control of laser plasma instabilities using Spike Trains of Uneven Duration and Delay (STUD pulses) for ICF and IFE

    Science.gov (United States)

    Afeyan, Bedros; Hüller, Stefan

    2013-11-01

    An adaptive method of controlling parametric instabilities in laser produced plasmas is proposed. It involves fast temporal modulation of a laser pulse on the fastest instability's amplification time scale, adapting to changing and unknown plasma conditions. These pulses are comprised of on and off sequences having at least one or two orders of magnitude contrast between them. Such laser illumination profiles are called STUD pulses for Spike Trains of Uneven Duration and Delay. The STUD pulse program includes scrambling the speckle patterns spatially in between the laser spikes. The off times allow damping of driven waves. The scrambling of the hot spots allows tens of damping times to elapse before hot spot locations experience recurring high intensity spikes. Damping in the meantime will have healed the scars of past growth. Another unique feature of STUD pulses on crossing beams is that their temporal profiles can be interlaced or staggered, and their interactions thus controlled with an on-off switch and a dimmer.

  15. Novel Dynamics Observed in a Spiking Neural Network Model of the NTS in the Rat Hind-brain

    Science.gov (United States)

    Zhou, Jingyi; Schaffer, J. David; Dilorenzo, Patricia; Laramee, Craig

    2012-02-01

    The Nucleus of the Solitary Tract (NTS) is a hind-brain structure in the rat that is the first way-station in taste processing. Its structure and function are poorly understood. Recently our group produced a model, implemented as a spiking neural network (SNN), that successfully replicated experimental data. The model's topology was manually devised and the parameters were set by a genetic algorithm. In order to better understand its information processing capabilities, we probed the model with a variety of input spike patterns and observed a striking winner-take-all decision-making dynamic. We show how the topology and tuned parameters enable this decision to depend on precise spike timing events. It is curious that the experimental data upon which the model was originally evolved did not include winner-take-all examples; this was an emergent capability. It remains for additional experiments on rats to confirm or reject this model prediction.

  16. Optimal design for hetero-associative memory: hippocampal CA1 phase response curve and spike-timing-dependent plasticity.

    Directory of Open Access Journals (Sweden)

    Ryota Miyata

    Full Text Available Recently reported experimental findings suggest that the hippocampal CA1 network stores spatio-temporal spike patterns and retrieves temporally reversed and spread-out patterns. In this paper, we explore the idea that the properties of the neural interactions and the synaptic plasticity rule in the CA1 network enable it to function as a hetero-associative memory recalling such reversed and spread-out spike patterns. In line with Lengyel's speculation (Lengyel et al., 2005, we firstly derive optimally designed spike-timing-dependent plasticity (STDP rules that are matched to neural interactions formalized in terms of phase response curves (PRCs for performing the hetero-associative memory function. By maximizing object functions formulated in terms of mutual information for evaluating memory retrieval performance, we search for STDP window functions that are optimal for retrieval of normal and doubly spread-out patterns under the constraint that the PRCs are those of CA1 pyramidal neurons. The system, which can retrieve normal and doubly spread-out patterns, can also retrieve reversed patterns with the same quality. Finally, we demonstrate that purposely designed STDP window functions qualitatively conform to typical ones found in CA1 pyramidal neurons.

  17. Limits of spiked random matrices II

    CERN Document Server

    Bloemendal, Alex

    2011-01-01

    The top eigenvalues of rank r spiked real Wishart matrices and additively perturbed Gaussian orthogonal ensembles are known to exhibit a phase transition in the large size limit. We show that they have limiting distributions for near-critical perturbations, fully resolving the conjecture of Baik, Ben Arous and P\\'ech\\'e (2005). The starting point is a new (2r+1)-diagonal form that is algebraically natural to the problem; for both models it converges to a certain random Schr\\"odinger operator on the half-line with r x r matrix-valued potential. The perturbation determines the boundary condition, and the low-lying eigenvalues describe the limit jointly over all perturbations in a fixed subspace. We treat the real, complex and quaternion (beta = 1,2,4) cases simultaneously. We also characterize the limit laws in terms of a diffusion related to Dyson's Brownian motion, and further in terms of a linear parabolic PDE; here beta is simply a parameter. At beta = 2 the PDE appears to reconcile with known Painlev\\'e fo...

  18. Studying a Chaotic Spiking Neural Model

    Directory of Open Access Journals (Sweden)

    Mohammad Alhawarat

    2013-09-01

    Full Text Available Dynamics of a chaotic spiking neuron model are being studied mathematically and experimentally. The Nonlinear Dynamic State neuron (NDS is analysed to further understand the model and improve it.Chaos has many interesting properties such as sensitivity to initial conditions, space filling, control and synchronization. As suggested by biologists, these properties may be exploited and play vital role in carrying out computational tasks in human brain. The NDS model has some limitations; in thus paper the model is investigated to overcomesome of these limitations in order to enhance the model. Therefore,the model’s parameters are tuned and the resulted dynamics are studied. Also, the discretization method of the model is considered. Moreover, a mathematical analysis is carried out to reveal the underlying dynamics of the model after tuning of its parameters. The results of the aforementioned methods revealedsome facts regarding the NDS attractor and suggest the stabilization of a large number of unstable periodic orbits (UPOs which might correspond to memories in phase space

  19. Uroguanylin induces electroencephalographic spikes in rats

    Directory of Open Access Journals (Sweden)

    MDA. Teixeira

    Full Text Available Uroguanylin (UGN is an endogenous peptide that acts on membrane-bound guanylate cyclase receptors of intestinal and renal cells increasing cGMP production and regulating electrolyte and water epithelial transport. Recent research works demonstrate the expression of this peptide and its receptor in the central nervous system. The current work was undertaken in order to evaluate modifications of electroencephalographic spectra (EEG in anesthetized Wistar rats, submitted to intracisternal infusion of uroguanylin (0.0125 nmoles/min or 0.04 nmoles/min. The current observations demonstrate that 0.0125 nmoles/min and 0.04 nmoles/min intracisternal infusion of UGN significantly enhances amplitude and frequency of sharp waves and evoked spikes (p = 0.03. No statistical significance was observed on absolute alpha and theta spectra amplitude. The present data suggest that UGN acts on bioelectrogenesis of cortical cells by inducing hypersynchronic firing of neurons. This effect is blocked by nedocromil, suggesting that UGN acts by increasing the activity of chloride channels.

  20. Uroguanylin induces electroencephalographic spikes in rats.

    Science.gov (United States)

    Teixeira, M D A; Nascimento, N R F; Fonteles, M C; Vale, O C

    2013-08-01

    Uroguanylin (UGN) is an endogenous peptide that acts on membrane-bound guanylate cyclase receptors of intestinal and renal cells increasing cGMP production and regulating electrolyte and water epithelial transport. Recent research works demonstrate the expression of this peptide and its receptor in the central nervous system. The current work was undertaken in order to evaluate modifications of electroencephalographic spectra (EEG) in anesthetized Wistar rats, submitted to intracisternal infusion of uroguanylin (0.0125 nmoles/min or 0.04 nmoles/min). The current observations demonstrate that 0.0125 nmoles/min and 0.04 nmoles/min intracisternal infusion of UGN significantly enhances amplitude and frequency of sharp waves and evoked spikes (p = 0.03). No statistical significance was observed on absolute alpha and theta spectra amplitude. The present data suggest that UGN acts on bioelectrogenesis of cortical cells by inducing hypersynchronic firing of neurons. This effect is blocked by nedocromil, suggesting that UGN acts by increasing the activity of chloride channels.

  1. STUDYING A CHAOTIC SPIKING NEURAL MODEL

    Directory of Open Access Journals (Sweden)

    Mohammad Alhawarat

    2013-09-01

    Full Text Available Dynamics of a chaotic spiking neuron model are being studied mathematically and experimentally. The Nonlinear Dynamic State neuron (NDS is analysed to further understand the model and improve it. Chaos has many interesting properties such as sensitivity to initial conditions, space filling, control and synchronization. As suggested by biologists, these properties may be exploited and play vital role in carrying out computational tasks in human brain. The NDS model has some limitations; in thus paper the model is investigated to overcome some of these limitations in order to enhance the model. Therefore, the model’s parameters are tuned and the resulted dynamics are studied. Also, the discretization method of the model is considered. Moreover, a mathematical analysis is carried out to reveal the underlying dynamics of the model after tuning of its parameters. The results of the aforementioned methods revealed some facts regarding the NDS attractor and suggest the stabilization of a large number of unstable periodic orbits (UPOs which might correspond to memories in phase space.

  2. A method for decoding the neurophysiological spike-response transform.

    Science.gov (United States)

    Stern, Estee; García-Crescioni, Keyla; Miller, Mark W; Peskin, Charles S; Brezina, Vladimir

    2009-11-15

    Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.

  3. Cytosolic pro-apoptotic SPIKE induces mitochondrial apoptosis in cancer.

    Science.gov (United States)

    Nikolic, Ivana; Kastratovic, Tatjana; Zelen, Ivanka; Zivanovic, Aleksandar; Arsenijevic, Slobodan; Mitrovic, Marina

    2010-04-30

    Proteins of the BCL-2 family are important regulators of apoptosis. The BCL-2 family includes three main subgroups: the anti-apoptotic group, such as BCL-2, BCL-XL, BCL-W, and MCL-1; multi-domain pro-apoptotic BAX, BAK; and pro-apoptotic "BH3-only" BIK, PUMA, NOXA, BID, BAD, and SPIKE. SPIKE, a rare pro-apoptotic protein, is highly conserved throughout the evolution, including Caenorhabditis elegans, whose expression is downregulated in certain tumors, including kidney, lung, and breast. In the literature, SPIKE was proposed to interact with BAP31 and prevent BCL-XL from binding to BAP31. Here, we utilized the Position Weight Matrix method to identify SPIKE to be a BH3-only pro-apoptotic protein mainly localized in the cytosol of all cancer cell lines tested. Overexpression of SPIKE weakly induced apoptosis in comparison to the known BH3-only pro-apoptotic protein BIK. SPIKE promoted mitochondrial cytochrome c release, the activation of caspase 3, and the caspase cleavage of caspase's downstream substrates BAP31 and p130CAS. Although the informatics analysis of SPIKE implicates this protein as a member of the BH3-only BCL-2 subfamily, its role in apoptosis remains to be elucidated.

  4. Statistical technique for analysing functional connectivity of multiple spike trains.

    Science.gov (United States)

    Masud, Mohammad Shahed; Borisyuk, Roman

    2011-03-15

    A new statistical technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an "influence function" is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of "common source" or "indirect" connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains.

  5. Controlling chaos in balanced neural circuits with input spike trains

    Science.gov (United States)

    Engelken, Rainer; Wolf, Fred

    The cerebral cortex can be seen as a system of neural circuits driving each other with spike trains. Here we study how the statistics of these spike trains affects chaos in balanced target circuits.Earlier studies of chaos in balanced neural circuits either used a fixed input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We study dynamical stability of balanced networks driven by input spike trains with variable statistics. The analytically obtained Jacobian enables us to calculate the complete Lyapunov spectrum. We solved the dynamics in event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We vary correlations, irregularity, coupling strength and spike rate of the input and action potential onset rapidness of recurrent neurons.We generally find a suppression of chaos by input spike trains. This is strengthened by bursty and correlated input spike trains and increased action potential onset rapidness. We find a link between response reliability and the Lyapunov spectrum. Our study extends findings in chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of projections in shaping the dynamics of large neural circuits.

  6. The Organization of Repetitive DNA in the Genomes of Amazonian Lizard Species in the Family Teiidae.

    Science.gov (United States)

    Carvalho, Natalia D M; Pinheiro, Vanessa S S; Carmo, Edson J; Goll, Leonardo G; Schneider, Carlos H; Gross, Maria C

    2015-01-01

    Repetitive DNA is the largest fraction of the eukaryote genome and comprises tandem and dispersed sequences. It presents variations in relation to its composition, number of copies, distribution, dynamics, and genome organization, and participates in the evolutionary diversification of different vertebrate species. Repetitive sequences are usually located in the heterochromatin of centromeric and telomeric regions of chromosomes, contributing to chromosomal structures. Therefore, the aim of this study was to physically map repetitive DNA sequences (5S rDNA, telomeric sequences, tropomyosin gene 1, and retroelements Rex1 and SINE) of mitotic chromosomes of Amazonian species of teiids (Ameiva ameiva, Cnemidophorus sp. 1, Kentropyx calcarata, Kentropyx pelviceps, and Tupinambis teguixin) to understand their genome organization and karyotype evolution. The mapping of repetitive sequences revealed a distinct pattern in Cnemidophorus sp. 1, whereas the other species showed all sequences interspersed in the heterochromatic region. Physical mapping of the tropomyosin 1 gene was performed for the first time in lizards and showed that in addition to being functional, this gene has a structural function similar to the mapped repetitive elements as it is located preferentially in centromeric regions and termini of chromosomes.

  7. Robust Repetitive Controller for Fast AFM Imaging

    CERN Document Server

    Necipoglu, Serkan; Has, Yunus; Guvenc, Levent; Basdogan, Cagatay

    2012-01-01

    Currently, Atomic Force Microscopy (AFM) is the most preferred Scanning Probe Microscopy (SPM) method due to its numerous advantages. However, increasing the scanning speed and reducing the interaction forces between the probe's tip and the sample surface are still the two main challenges in AFM. To meet these challenges, we take advantage of the fact that the lateral movements performed during an AFM scan is a repetitive motion and propose a Repetitive Controller (RC) for the z-axis movements of the piezo-scanner. The RC utilizes the profile of the previous scan line while scanning the current line to achieve a better scan performance. The results of the scanning experiments performed with our AFM set-up show that the proposed RC significantly outperforms a conventional PI controller that is typically used for the same task. The scan error and the average tapping forces are reduced by 66% and 58%, respectively when the scan speed is increased by 7-fold.

  8. Repetition blindness for natural images of objects with viewpoint changes

    Directory of Open Access Journals (Sweden)

    Stephane eBuffat

    2013-01-01

    Full Text Available When stimuli are repeated in a rapid serial visual presentation (RSVP, observers sometimes fail to report the second occurrence of a target. This phenomenon is referred to as repetition blindness (RB. We report an RSVP experiment with photographs in which we manipulated object viewpoints between the first and second occurrences of a target (0-, 45-, or 90-degree changes, and spatial frequency content. Natural images were spatially filtered to produce low, medium, or high spatial-frequency stimuli. RB was observed for all filtering conditions. Surprisingly, for full-spectrum images, RB increased significantly as the viewpoint reached 90 degrees. For filtered images, a similar pattern of results was found for all conditions except for medium spatial-frequency stimuli. These findings suggest that object recognition in RSVP are subtended by viewpoint-specific representations for all spatial frequencies except medium ones.

  9. Layer I neurons of rat neocortex. I. Action potential and repetitive firing properties.

    Science.gov (United States)

    Zhou, F M; Hablitz, J J

    1996-08-01

    1. Whole cell patch-clamp techniques, combined with direct visualization of neurons, were used to study action potential (AP) and repetitive firing properties of layer I neurons in slices of rat neocortex. 2. Layer I neurons had resting membrane potentials (RMP) of -59.8 +/- 4.7 mV (mean +/- SD) and input resistances (RN) of 592 +/- 284 M Omega. Layer II/III pyramidal neurons had RMPs and RNs of -61.5 +/- 5.6 mV and 320 +/- 113 M omega, respectively. A double exponential function was needed to describe the charging curves of both neuron types. In layer I neurons, tau(0) was 45 +/- 22 ms and tau(1) was 5 +/- 3.3 ms whereas in layer II/III pyramidal neurons, tau(0) was 41 +/- 11 ms and tau(1) was 3 +/- 2.6 ms. Estimates of specific membrane resistance (Rm) for layer I and layer II/III cells were 45 +/- 22 and 41 +/- 11 k omega cm2, respectively (Cm was assumed to be 1 microF/cm2). 3. AP threshold was -41 +/- 2 mV in layer I neurons. Spike amplitudes, measured from threshold to peak, were 90.6 +/- 7.7 mV. AP durations, measured both at the base and half maximal amplitude, were 2.5 +/- 0.4 and 1.1 +/- 0.2 ms, respectively. AP 10-90% rise and repolarization times were 0.6 +/- 0.1 and 1.1 +/- 0.2 ms, respectively. In layer II/III pyramidal neurons, AP threshold was -41 +/- 2.5 mV and spike amplitude was 97 +/- 9.7 mV. AP duration at base and half maximal amplitude was 5.4 +/- 1.1 ms and 1.8 +/- 0.2 ms, respectively. AP 10-90% rise and decay times were 0.6 +/- 0.1 ms and 2.8 +/- 0.6 ms, respectively. 4. Layer I neurons were fast spiking cells that showed little frequency adaptation, a large fast afterhyperpolarization (fAHP), and no slow afterhyperpolarization (sAHP). Some cells had a medium afterhyperpolarization (mAHP) and a slow afterdepolarization (sADP). All pyramidal cells in layer II/III and "atypical" pyramidal neurons in upper layer II showed regular spiking behavior, prominent frequency adaptation, and marked sAHPs. 5. In both layer I neurons and layer II

  10. A repetitive elements perspective in Polycomb epigenetics.

    Directory of Open Access Journals (Sweden)

    Valentina eCasa

    2012-10-01

    Full Text Available Repetitive elements comprise over two-thirds of the human genome. For a long time, these elements have received little attention since they were considered non functional. On the contrary, recent evidence indicates that they play central roles in genome integrity, gene expression and disease. Indeed, repeats display meiotic instability associated with disease and are located within common fragile sites, which are hotspots of chromosome rearrangements in tumors. Moreover, a variety of diseases have been associated with aberrant transcription of repetitive elements. Overall this indicates that appropriate regulation of repetitive elements’ activity is fundamental.Polycomb group (PcG proteins are epigenetic regulators that are essential for the normal development of multicellular organisms. Mammalian PcG proteins are involved in fundamental processes, such as cellular memory, cell proliferation, genomic imprinting, X-inactivation, and cancer development. PcG proteins can convey their activity through long-distance interactions also on different chromosomes. This indicates that the 3D organization of PcG proteins contributes significantly to their function. However, it is still unclear how these complex mechanisms are orchestrated and which role PcG proteins play in the multi-level organization of gene regulation. Intriguingly, the greatest proportion of Polycomb-mediated chromatin modifications is located in genomic repeats and it has been suggested that they could provide a binding platform for Polycomb proteins.Here, these lines of evidence are woven together to discuss how repetitive elements could contribute to chromatin organization in the 3D nuclear space.

  11. Emotional arousal enhances word repetition priming

    OpenAIRE

    Thomas, Laura A.; LaBar, Kevin S.

    2005-01-01

    Three experiments were conducted to determine if emotional content increases repetition priming magnitude. In the study phase of Experiment 1, participants rated high-arousing negative (taboo) words and neutral words for concreteness. In the test phase, they made lexical decision judgements for the studied words intermixed with novel words (half taboo, half neutral) and pseudowords. In Experiment 2, low-arousing negative (LAN) words were substituted for the taboo words, and in Experiment 3 al...

  12. The Rhythms of Echo. Variations on Repetition

    Directory of Open Access Journals (Sweden)

    Rosa María Aradra Sánchez

    2015-04-01

    Full Text Available This paper presents a study on the echo as metric and rhetorical procedure. It makes a brief tour through some of the poetic manifestations of echo in the Spanish literary tradition, and a brief tour through the attention that metric theory has paid to this phenomenon. Then it stops at the possibilities that rhetoric offers for its analysis from the generic approach of the discursive repetition phenomena.

  13. Repetitive behaviour in autism: Imaging pathways and trajectories

    NARCIS (Netherlands)

    Langen, M.J.G.

    2009-01-01

    Repetitive behaviour in autism: Imaging pathways and trajectories Repetitive and rigid behaviour is one of the core symptoms of autism, a severe and lifelong child psychiatric disorder. Although repetitive behaviour symptoms often form a significant impairment for affected individuals, systematic st

  14. Neural Correlates of Restricted, Repetitive Behaviors in Autism Spectrum Disorders

    Science.gov (United States)

    2014-12-01

    Restrictive Repetitive Behaviors in Autism Spectrum Disorder . Authors: T.Q.Nguyen, B...Manoach. Functional Connectivity of the Dorsal Anterior Cingulate Cortex Predicts Restrictive Repetitive Behaviors in Autism Spectrum Disorder We...Introduction: Although restricted , repetitive behaviors (RRBs) are a highly disabling core feature of Autism Spectrum Disorders (ASDs), they

  15. Lingual Kinematics during Rapid Syllable Repetition in Parkinson's Disease

    Science.gov (United States)

    Wong, Min Ney; Murdoch, Bruce E.; Whelan, Brooke-Mai

    2012-01-01

    Background: Rapid syllable repetition tasks are commonly used in the assessment of motor speech disorders. However, little is known about the articulatory kinematics during rapid syllable repetition in individuals with Parkinson's disease (PD). Aims: To investigate and compare lingual kinematics during rapid syllable repetition in dysarthric…

  16. Repetitive Elements in Mycoplasma hyopneumoniae Transcriptional Regulation

    Science.gov (United States)

    Cattani, Amanda Malvessi; Siqueira, Franciele Maboni; Guedes, Rafael Lucas Muniz; Schrank, Irene Silveira

    2016-01-01

    Transcriptional regulation, a multiple-step process, is still poorly understood in the important pig pathogen Mycoplasma hyopneumoniae. Basic motifs like promoters and terminators have already been described, but no other cis-regulatory elements have been found. DNA repeat sequences have been shown to be an interesting potential source of cis-regulatory elements. In this work, a genome-wide search for tandem and palindromic repetitive elements was performed in the intergenic regions of all coding sequences from M. hyopneumoniae strain 7448. Computational analysis demonstrated the presence of 144 tandem repeats and 1,171 palindromic elements. The DNA repeat sequences were distributed within the 5’ upstream regions of 86% of transcriptional units of M. hyopneumoniae strain 7448. Comparative analysis between distinct repetitive sequences found in related mycoplasma genomes demonstrated different percentages of conservation among pathogenic and nonpathogenic strains. qPCR assays revealed differential expression among genes showing variable numbers of repetitive elements. In addition, repeats found in 206 genes already described to be differentially regulated under different culture conditions of M. hyopneumoniae strain 232 showed almost 80% conservation in relation to M. hyopneumoniae strain 7448 repeats. Altogether, these findings suggest a potential regulatory role of tandem and palindromic DNA repeats in the M. hyopneumoniae transcriptional profile. PMID:28005945

  17. Repetitive element hypermethylation in multiple sclerosis patients.

    Science.gov (United States)

    Neven, K Y; Piola, M; Angelici, L; Cortini, F; Fenoglio, C; Galimberti, D; Pesatori, A C; Scarpini, E; Bollati, V

    2016-06-18

    Multiple sclerosis (MS) is a complex disorder of the central nervous system whose cause is currently unknown. Evidence is increasing that DNA methylation alterations could be involved in inflammatory and neurodegenerative diseases and could contribute to MS pathogenesis. Repetitive elements Alu, LINE-1 and SAT-α, are widely known as estimators of global DNA methylation. We investigated Alu, LINE-1 and SAT-α methylation levels to evaluate their difference in a case-control setup and their role as a marker of disability. We obtained blood samples from 51 MS patients and 137 healthy volunteers matched by gender, age and smoking. Methylation was assessed using bisulfite-PCR-pyrosequencing. For all participants, medical history, physical and neurological examinations and screening laboratory tests were collected. All repetitive elements were hypermethylated in MS patients compared to healthy controls. A lower Expanded Disability Status Scale (EDSS) score was associated with a lower levels of LINE-1 methylation for 'EDSS = 1.0' and '1.5 ≤ EDSS ≤ 2.5' compared to an EDSS higher than 3, while Alu was associated with a higher level of methylation in these groups: 'EDSS = 1.0' and '1.5 ≤ EDSS ≤ 2.5'. MS patients exhibit an hypermethylation in repetitive elements compared to healthy controls. Alu and LINE-1 were associated with degree of EDSS score. Forthcoming studies focusing on epigenetics and the multifactorial pathogenetic mechanism of MS could elucidate these links further.

  18. FRB repetition and non-Poissonian statistics

    CERN Document Server

    Connor, Liam; Oppermann, Niels

    2016-01-01

    We discuss some of the claims that have been made regarding the statistics of fast radio bursts (FRBs). In an earlier paper \\citep{2015arXiv150505535C} we conjectured that flicker noise associated with FRB repetition could show up in non-cataclysmic neutron star emission models, like supergiant pulses. We show how the current limits of repetition would be significantly weakened if their repeat rate really were non-Poissonian and had a pink or red spectrum. Repetition and its statistics have implications for observing strategy, generally favouring shallow wide-field surveys, since in the non-repeating scenario survey depth is unimportant. We also discuss the statistics of the apparent latitudinal dependence of FRBs, and offer a simple method for calculating the significance of this effect. We provide a generalized Bayesian framework for addressing this problem, which allows for direct model comparison. It is shown how the evidence for a steep latitudinal gradient of the FRB rate is less strong than initially s...

  19. Timing intervals using population synchrony and spike timing dependent plasticity

    Directory of Open Access Journals (Sweden)

    Wei Xu

    2016-12-01

    Full Text Available We present a computational model by which ensembles of regularly spiking neurons can encode different time intervals through synchronous firing. We show that a neuron responding to a large population of convergent inputs has the potential to learn to produce an appropriately-timed output via spike-time dependent plasticity. We explain why temporal variability of this population synchrony increases with increasing time intervals. We also show that the scalar property of timing and its violation at short intervals can be explained by the spike-wise accumulation of jitter in the inter-spike intervals of timing neurons. We explore how the challenge of encoding longer time intervals can be overcome and conclude that this may involve a switch to a different population of neurons with lower firing rate, with the added effect of producing an earlier bias in response. Experimental data on human timing performance show features in agreement with the model’s output.

  20. Efficiently passing messages in distributed spiking neural network simulation.

    Science.gov (United States)

    Thibeault, Corey M; Minkovich, Kirill; O'Brien, Michael J; Harris, Frederick C; Srinivasa, Narayan

    2013-01-01

    Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI). A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid method for spike exchange was implemented and benchmarked.

  1. Preterm Birth Risk Spikes in Mothers with Sleep Disorders

    Science.gov (United States)

    ... https://medlineplus.gov/news/fullstory_167666.html Preterm Birth Risk Spikes in Mothers With Sleep Disorders Pregnant ... during pregnancy may increase the risk of preterm birth, a new study finds. The California research looked ...

  2. Benchmarking Spike Rate Inference in Population Calcium Imaging.

    Science.gov (United States)

    Theis, Lucas; Berens, Philipp; Froudarakis, Emmanouil; Reimer, Jacob; Román Rosón, Miroslav; Baden, Tom; Euler, Thomas; Tolias, Andreas S; Bethge, Matthias

    2016-05-01

    A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.

  3. Hardware/Software Co-Design for Spike Based Recognition

    CERN Document Server

    Ghani, Arfan; Maguire, Liam; Harkin, Jim

    2008-01-01

    The practical applications based on recurrent spiking neurons are limited due to their non-trivial learning algorithms. The temporal nature of spiking neurons is more favorable for hardware implementation where signals can be represented in binary form and communication can be done through the use of spikes. This work investigates the potential of recurrent spiking neurons implementations on reconfigurable platforms and their applicability in temporal based applications. A theoretical framework of reservoir computing is investigated for hardware/software implementation. In this framework, only readout neurons are trained which overcomes the burden of training at the network level. These recurrent neural networks are termed as microcircuits which are viewed as basic computational units in cortical computation. This paper investigates the potential of recurrent neural reservoirs and presents a novel hardware/software strategy for their implementation on FPGAs. The design is implemented and the functionality is ...

  4. Balanced synaptic input shapes the correlation between neural spike trains.

    Science.gov (United States)

    Litwin-Kumar, Ashok; Oswald, Anne-Marie M; Urban, Nathaniel N; Doiron, Brent

    2011-12-01

    Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states.

  5. Balanced synaptic input shapes the correlation between neural spike trains.

    Directory of Open Access Journals (Sweden)

    Ashok Litwin-Kumar

    2011-12-01

    Full Text Available Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states.

  6. Higher Order Spike Synchrony in Prefrontal Cortex during visual memory

    Directory of Open Access Journals (Sweden)

    Gordon ePipa

    2011-06-01

    Full Text Available Precise temporal synchrony of spike firing has been postulated as an important neuronal mechanism for signal integration and the induction of plasticity in neocortex. As prefrontal cortex plays an important role in organizing memory and executive functions, the convergence of multiple visual pathways onto PFC predicts that neurons should preferentially synchronize their spiking when stimulus information is processed. Furthermore, synchronous spike firing should intensify if memory processes require the induction of neuronal plasticity, even if this is only for short-term. Here we show with multiple simultaneously recorded units in ventral prefrontal cortex that neurons participate in 3 ms precise synchronous discharges distributed across multiple sites separated by at least 500 µm. The frequency of synchronous firing is modulated by behavioral performance and is specific for the memorized visual stimuli. In particular, during the memory period in which activity is not stimulus driven, larger groups of up to 7 sites exhibit performance dependent modulation of their spike synchronization.

  7. Efficiently passing messages in distributed spiking neural network simulation

    Directory of Open Access Journals (Sweden)

    Corey Michael Thibeault

    2013-06-01

    Full Text Available Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI. A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid-method for spike exchange was implemented and benchmarked.

  8. Biological modelling of a computational spiking neural network with neuronal avalanches

    Science.gov (United States)

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-05-01

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

  9. Upper Limb Biomechanics During the Volleyball Serve and Spike

    OpenAIRE

    Reeser, Jonathan C.; Fleisig, Glenn S.; Bolt, Becky; Ruan, Mianfang

    2010-01-01

    Background: The shoulder is the third-most commonly injured body part in volleyball, with the majority of shoulder problems resulting from chronic overuse. Hypothesis: Significant kinetic differences exist among specific types of volleyball serves and spikes. Study Design: Controlled laboratory study. Methods: Fourteen healthy female collegiate volleyball players performed 5 successful trials of 4 skills: 2 directional spikes, an off-speed roll shot, and the float serve. Volunteers who were c...

  10. Contrasting the Chromosomal Organization of Repetitive DNAs in Two Gryllidae Crickets with Highly Divergent Karyotypes.

    Science.gov (United States)

    Palacios-Gimenez, Octavio M; Carvalho, Carlos Roberto; Ferrari Soares, Fernanda Aparecida; Cabral-de-Mello, Diogo C

    2015-01-01

    A large percentage of eukaryotic genomes consist of repetitive DNA that plays an important role in the organization, size and evolution. In the case of crickets, chromosomal variability has been found using classical cytogenetics, but almost no information concerning the organization of their repetitive DNAs is available. To better understand the chromosomal organization and diversification of repetitive DNAs in crickets, we studied the chromosomes of two Gryllidae species with highly divergent karyotypes, i.e., 2n(♂) = 29,X0 (Gryllus assimilis) and 2n = 9, neo-X1X2Y (Eneoptera surinamensis). The analyses were performed using classical cytogenetic techniques, repetitive DNA mapping and genome-size estimation. Conserved characteristics were observed, such as the occurrence of a small number of clusters of rDNAs and U snDNAs, in contrast to the multiple clusters/dispersal of the H3 histone genes. The positions of U2 snDNA and 18S rDNA are also conserved, being intermingled within the largest autosome. The distribution and base-pair composition of the heterochromatin and repetitive DNA pools of these organisms differed, suggesting reorganization. Although the microsatellite arrays had a similar distribution pattern, being dispersed along entire chromosomes, as has been observed in some grasshopper species, a band-like pattern was also observed in the E. surinamensis chromosomes, putatively due to their amplification and clustering. In addition to these differences, the genome of E. surinamensis is approximately 2.5 times larger than that of G. assimilis, which we hypothesize is due to the amplification of repetitive DNAs. Finally, we discuss the possible involvement of repetitive DNAs in the differentiation of the neo-sex chromosomes of E. surinamensis, as has been reported in other eukaryotic groups. This study provided an opportunity to explore the evolutionary dynamics of repetitive DNAs in two non-model species and will contribute to the understanding of

  11. Contrasting the Chromosomal Organization of Repetitive DNAs in Two Gryllidae Crickets with Highly Divergent Karyotypes.

    Directory of Open Access Journals (Sweden)

    Octavio M Palacios-Gimenez

    Full Text Available A large percentage of eukaryotic genomes consist of repetitive DNA that plays an important role in the organization, size and evolution. In the case of crickets, chromosomal variability has been found using classical cytogenetics, but almost no information concerning the organization of their repetitive DNAs is available. To better understand the chromosomal organization and diversification of repetitive DNAs in crickets, we studied the chromosomes of two Gryllidae species with highly divergent karyotypes, i.e., 2n(♂ = 29,X0 (Gryllus assimilis and 2n = 9, neo-X1X2Y (Eneoptera surinamensis. The analyses were performed using classical cytogenetic techniques, repetitive DNA mapping and genome-size estimation. Conserved characteristics were observed, such as the occurrence of a small number of clusters of rDNAs and U snDNAs, in contrast to the multiple clusters/dispersal of the H3 histone genes. The positions of U2 snDNA and 18S rDNA are also conserved, being intermingled within the largest autosome. The distribution and base-pair composition of the heterochromatin and repetitive DNA pools of these organisms differed, suggesting reorganization. Although the microsatellite arrays had a similar distribution pattern, being dispersed along entire chromosomes, as has been observed in some grasshopper species, a band-like pattern was also observed in the E. surinamensis chromosomes, putatively due to their amplification and clustering. In addition to these differences, the genome of E. surinamensis is approximately 2.5 times larger than that of G. assimilis, which we hypothesize is due to the amplification of repetitive DNAs. Finally, we discuss the possible involvement of repetitive DNAs in the differentiation of the neo-sex chromosomes of E. surinamensis, as has been reported in other eukaryotic groups. This study provided an opportunity to explore the evolutionary dynamics of repetitive DNAs in two non-model species and will contribute to the

  12. The Omega-Infinity Limit of Single Spikes

    CERN Document Server

    Axenides, Minos; Linardopoulos, Georgios

    2016-01-01

    A new infinite-size limit of strings in RxS2 is presented. The limit is obtained from single spike strings by letting their angular velocity omega become infinite. We derive the energy-momenta relation of omega-infinity single spikes as their linear velocity v-->1 and their angular momentum J-->1. Generally, the v-->1, J-->1 limit of single spikes is singular and has to be excluded from the spectrum and be studied separately. We discover that the dispersion relation of omega-infinity single spikes contains logarithms in the limit J-->1. This result is somewhat surprising, since the logarithmic behavior in the string spectra is typically associated with their motion in non-compact spaces such as AdS. Omega-infinity single spikes seem to completely cover the surface of the 2-sphere they occupy, so that they may essentially be viewed as some sort of "brany strings". A proof of the sphere-filling property of omega-infinity single spikes is given in the appendix.

  13. Neuronal spike initiation modulated by extracellular electric fields.

    Directory of Open Access Journals (Sweden)

    Guo-Sheng Yi

    Full Text Available Based on a reduced two-compartment model, the dynamical and biophysical mechanism underlying the spike initiation of the neuron to extracellular electric fields is investigated in this paper. With stability and phase plane analysis, we first investigate in detail the dynamical properties of neuronal spike initiation induced by geometric parameter and internal coupling conductance. The geometric parameter is the ratio between soma area and total membrane area, which describes the proportion of area occupied by somatic chamber. It is found that varying it could qualitatively alter the bifurcation structures of equilibrium as well as neuronal phase portraits, which remain unchanged when varying internal coupling conductance. By analyzing the activating properties of somatic membrane currents at subthreshold potentials, we explore the relevant biophysical basis of spike initiation dynamics induced by these two parameters. It is observed that increasing geometric parameter could greatly decrease the intensity of the internal current flowing from soma to dendrite, which switches spike initiation dynamics from Hopf bifurcation to SNIC bifurcation; increasing internal coupling conductance could lead to the increase of this outward internal current, whereas the increasing range is so small that it could not qualitatively alter the spike initiation dynamics. These results highlight that neuronal geometric parameter is a crucial factor in determining the spike initiation dynamics to electric fields. The finding is useful to interpret the functional significance of neuronal biophysical properties in their encoding dynamics, which could contribute to uncovering how neuron encodes electric field signals.

  14. Estimating nonstationary input signals from a single neuronal spike train.

    Science.gov (United States)

    Kim, Hideaki; Shinomoto, Shigeru

    2012-11-01

    Neurons temporally integrate input signals, translating them into timed output spikes. Because neurons nonperiodically emit spikes, examining spike timing can reveal information about input signals, which are determined by activities in the populations of excitatory and inhibitory presynaptic neurons. Although a number of mathematical methods have been developed to estimate such input parameters as the mean and fluctuation of the input current, these techniques are based on the unrealistic assumption that presynaptic activity is constant over time. Here, we propose tracking temporal variations in input parameters with a two-step analysis method. First, nonstationary firing characteristics comprising the firing rate and non-Poisson irregularity are estimated from a spike train using a computationally feasible state-space algorithm. Then, information about the firing characteristics is converted into likely input parameters over time using a transformation formula, which was constructed by inverting the neuronal forward transformation of the input current to output spikes. By analyzing spike trains recorded in vivo, we found that neuronal input parameters are similar in the primary visual cortex V1 and middle temporal area, whereas parameters in the lateral geniculate nucleus of the thalamus were markedly different.

  15. Automatic fitting of spiking neuron models to electrophysiological recordings

    Directory of Open Access Journals (Sweden)

    Cyrille Rossant

    2010-03-01

    Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.

  16. A neuromorphic VLSI design for spike timing and rate based synaptic plasticity.

    Science.gov (United States)

    Rahimi Azghadi, Mostafa; Al-Sarawi, Said; Abbott, Derek; Iannella, Nicolangelo

    2013-09-01

    Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological experiments, while the PSTDP rule fails to reproduce them. Additionally, it has been shown that the behaviour inherent to the spike rate-based Bienenstock-Cooper-Munro (BCM) synaptic plasticity rule can also emerge from the TSTDP rule. This paper proposes an analogue implementation of the TSTDP rule. The proposed VLSI circuit has been designed using the AMS 0.35 μm CMOS process and has been simulated using design kits for Synopsys and Cadence tools. Simulation results demonstrate how well the proposed circuit can alter synaptic weights according to the timing difference amongst a set of different patterns of spikes. Furthermore, the circuit is shown to give rise to a BCM-like learning rule, which is a rate-based rule. To mimic an implementation environment, a 1000 run Monte Carlo (MC) analysis was conducted on the proposed circuit. The presented MC simulation analysis and the simulation result from fine-tuned circuits show that it is possible to mitigate the effect of process variations in the proof of concept circuit; however, a practical variation aware design technique is required to promise a high circuit performance in a large scale neural network. We believe that the proposed design can play a significant role in future VLSI implementations of both spike timing and rate based neuromorphic learning systems.

  17. Estimating parameters of generalized integrate-and-fire neurons from the maximum likelihood of spike trains.

    Science.gov (United States)

    Dong, Yi; Mihalas, Stefan; Russell, Alexander; Etienne-Cummings, Ralph; Niebur, Ernst

    2011-11-01

    When a neuronal spike train is observed, what can we deduce from it about the properties of the neuron that generated it? A natural way to answer this question is to make an assumption about the type of neuron, select an appropriate model for this type, and then choose the model parameters as those that are most likely to generate the observed spike train. This is the maximum likelihood method. If the neuron obeys simple integrate-and-fire dynamics, Paninski, Pillow, and Simoncelli (2004) showed that its negative log-likelihood function is convex and that, at least in principle, its unique global minimum can thus be found by gradient descent techniques. Many biological neurons are, however, known to generate a richer repertoire of spiking behaviors than can be explained in a simple integrate-and-fire model. For instance, such a model retains only an implicit (through spike-induced currents), not an explicit, memory of its input; an example of a physiological situation that cannot be explained is the absence of firing if the input current is increased very slowly. Therefore, we use an expanded model (Mihalas & Niebur, 2009 ), which is capable of generating a large number of complex firing patterns while still being linear. Linearity is important because it maintains the distribution of the random variables and still allows maximum likelihood methods to be used. In this study, we show that although convexity of the negative log-likelihood function is not guaranteed for this model, the minimum of this function yields a good estimate for the model parameters, in particular if the noise level is treated as a free parameter. Furthermore, we show that a nonlinear function minimization method (r-algorithm with space dilation) usually reaches the global minimum.

  18. Different propagation speeds of recalled sequences in plastic spiking neural networks

    Science.gov (United States)

    Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

    2015-03-01

    Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in

  19. fMRI repetition suppression: neuronal adaptation or stimulus expectation?

    Science.gov (United States)

    Larsson, Jonas; Smith, Andrew T

    2012-03-01

    Measurements of repetition suppression with functional magnetic resonance imaging (fMRI adaptation) have been used widely to probe neuronal population response properties in human cerebral cortex. fMRI adaptation techniques assume that fMRI repetition suppression reflects neuronal adaptation, an assumption that has been challenged on the basis of evidence that repetition-related response changes may reflect unrelated factors, such as attention and stimulus expectation. Specifically, Summerfield et al. (Summerfield C, Trittschuh EH, Monti JM, Mesulam MM, Egner T. 2008. Neural repetition suppression reflects fulfilled perceptual expectations. Nat Neurosci. 11:1004-1006) reported that the relative frequency of stimulus repetitions and non-repetitions influenced the magnitude of repetition suppression in the fusiform face area, suggesting that stimulus expectation accounted for most of the effect of repetition. We confirm that stimulus expectation can significantly influence fMRI repetition suppression throughout visual cortex and show that it occurs with long as well as short adaptation durations. However, the effect was attention dependent: When attention was diverted away from the stimuli, the effects of stimulus expectation completely disappeared. Nonetheless, robust and significant repetition suppression was still evident. These results suggest that fMRI repetition suppression reflects a combination of neuronal adaptation and attention-dependent expectation effects that can be experimentally dissociated. This implies that with an appropriate experimental design, fMRI adaptation can provide valid measures of neuronal adaptation and hence response specificity.

  20. Evaluating the impact of sequencing error correction for RNA-seq data with ERCC RNA spike-in controls.

    Science.gov (United States)

    Tong, Li; Yang, Cheng; Wu, Po-Yen; Wang, May D

    2016-02-01

    Sequencing errors are a major issue for several next-generation sequencing-based applications such as de novo assembly and single nucleotide polymorphism detection. Several error-correction methods have been developed to improve raw data quality. However, error-correction performance is hard to evaluate because of the lack of a ground truth. In this study, we propose a novel approach which using ERCC RNA spike-in controls as the ground truth to facilitate error-correction performance evaluation. After aligning raw and corrected RNA-seq data, we characterized the quality of reads by three metrics: mismatch patterns (i.e., the substitution rate of A to C) of reads aligned with one mismatch, mismatch patterns of reads aligned with two mismatches and the percentage increase of reads aligned to reference. We observed that the mismatch patterns for reads aligned with one mismatch are significantly correlated between ERCC spike-ins and real RNA samples. Based on such observations, we conclude that ERCC spike-ins can serve as ground truths for error correction beyond their previous applications for validation of dynamic range and fold-change response. Also, the mismatch patterns for ERCC reads aligned with one mismatch can serve as a novel and reliable metric to evaluate the performance of error-correction tools.

  1. The Space-Clamped Hodgkin-Huxley System with Random Synaptic Input: Inhibition of Spiking by Weak Noise and Analysis with Moment Equations.

    Science.gov (United States)

    Tuckwell, Henry C; Ditlevsen, Susanne

    2016-10-01

    We consider a classical space-clamped Hodgkin-Huxley model neuron stimulated by synaptic excitation and inhibition with conductances represented by Ornstein-Uhlenbeck processes. Using numerical solutions of the stochastic model system obtained by an Euler method, it is found that with excitation only, there is a critical value of the steady-state excitatory conductance for repetitive spiking without noise, and for values of the conductance near the critical value, small noise has a powerfully inhibitory effect. For a given level of inhibition, there is also a critical value of the steady-state excitatory conductance for repetitive firing, and it is demonstrated that noise in either the excitatory or inhibitory processes or both can powerfully inhibit spiking. Furthermore, near the critical value, inverse stochastic resonance was observed when noise was present only in the inhibitory input process. The system of deterministic differential equations for the approximate first- and second-order moments of the model is derived. They are solved using Runge-Kutta methods, and the solutions are compared with the results obtained by simulation for various sets of parameters, including some with conductances obtained by experiment on pyramidal cells of rat prefrontal cortex. The mean and variance obtained from simulation are in good agreement when there is spiking induced by strong stimulation and relatively small noise or when the voltage is fluctuating at subthreshold levels. In the occasional spike mode sometimes exhibited by spinal motoneurons and cortical pyramidal cells, the assumptions underlying the moment equation approach are not satisfied. The simulation results show that noisy synaptic input of either an excitatory or inhibitory character or both may lead to the suppression of firing in neurons operating near a critical point and this has possible implications for cortical networks. Although suppression of firing is corroborated for the system of moment equations

  2. Inferring Neuronal Network Connectivity from Spike Data: A Temporal Datamining Approach

    CERN Document Server

    Patnaik, Debprakash; Unnikrishnan, K P

    2008-01-01

    Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity pa...

  3. Sums of Spike Waveform Features for Motor Decoding

    Directory of Open Access Journals (Sweden)

    Jie Li

    2017-07-01

    Full Text Available Traditionally, the key step before decoding motor intentions from cortical recordings is spike sorting, the process of identifying which neuron was responsible for an action potential. Recently, researchers have started investigating approaches to decoding which omit the spike sorting step, by directly using information about action potentials' waveform shapes in the decoder, though this approach is not yet widespread. Particularly, one recent approach involves computing the moments of waveform features and using these moment values as inputs to decoders. This computationally inexpensive approach was shown to be comparable in accuracy to traditional spike sorting. In this study, we use offline data recorded from two Rhesus monkeys to further validate this approach. We also modify this approach by using sums of exponentiated features of spikes, rather than moments. Our results show that using waveform feature sums facilitates significantly higher hand movement reconstruction accuracy than using waveform feature moments, though the magnitudes of differences are small. We find that using the sums of one simple feature, the spike amplitude, allows better offline decoding accuracy than traditional spike sorting by expert (correlation of 0.767, 0.785 vs. 0.744, 0.738, respectively, for two monkeys, average 16% reduction in mean-squared-error, as well as unsorted threshold crossings (0.746, 0.776; average 9% reduction in mean-squared-error. Our results suggest that the sums-of-features framework has potential as an alternative to both spike sorting and using unsorted threshold crossings, if developed further. Also, we present data comparing sorted vs. unsorted spike counts in terms of offline decoding accuracy. Traditional sorted spike counts do not include waveforms that do not match any template (“hash”, but threshold crossing counts do include this hash. On our data and in previous work, hash contributes to decoding accuracy. Thus, using the

  4. Aspartic acid aminotransferase activity is increased in actively spiking compared with non-spiking human epileptic cortex.

    Science.gov (United States)

    Kish, S J; Dixon, L M; Sherwin, A L

    1988-01-01

    Increased concentration of the excitatory neurotransmitter aspartic acid in actively spiking human epileptic cerebral cortex was recently described. In order to further characterise changes in the aspartergic system in epileptic brain, the behaviour of aspartic acid aminotransferase (AAT), a key enzyme involved in aspartic acid metabolism has now been examined. Electrocorticography performed during surgery was employed to identify cortical epileptic spike foci in 16 patients undergoing temporal lobectomy for intractable seizures. Patients with spontaneously spiking lateral temporal cortex (n = 8) were compared with a non-spiking control group (n = 8) of patients in whom the epileptic lesions were confined to the hippocampus sparing the temporal convexity. Mean activity of AAT in spiking cortex was significantly elevated by 16-18%, with aspartic acid concentration increased by 28%. Possible explanations for the enhanced AAT activity include increased proliferation of cortical AAT-containing astrocytes at the spiking focus and/or a generalised increase in neuronal or extraneuronal metabolism consequent to the ongoing epileptic discharge. It is suggested that the data provide additional support for a disturbance of central excitatory aspartic acid mechanisms in human epileptic brain. PMID:2898010

  5. Reversal learning in C58 mice: Modeling higher order repetitive behavior.

    Science.gov (United States)

    Whitehouse, Cristina M; Curry-Pochy, Lisa S; Shafer, Robin; Rudy, Joseph; Lewis, Mark H

    2017-08-14

    Restricted, repetitive behaviors are diagnostic for autism and prevalent in other neurodevelopmental disorders. These behaviors cluster as repetitive sensory-motor behaviors and behaviors reflecting resistance to change. The C58 mouse strain is a promising model for these behaviors as it emits high rates of aberrant repetitive sensory-motor behaviors. The purpose of the present study was to extend characterization of the C58 model to resistance to change. This was done by comparing C58 to C57BL/6 mice on a reversal learning task under either a 100% or 80%/20% probabilistic reinforcement schedule. In addition, the effect of environmental enrichment on performance of this task was assessed as this rearing condition markedly reduces repetitive sensory-motor behavior in C58 mice. Little difference was observed between C58 and control mice under a 100% schedule of reinforcement. The 80%/20% probabilistic schedule of reinforcement generated substantial strain differences, however. Importantly, no strain difference was observed in acquisition, but C58 mice were markedly impaired in their ability to reverse their pattern of responding from the previously high density reinforcement side. Environmental enrichment did not impact acquisition under the probabilistic reinforcement schedule, but enriched C58 mice performed significantly better than standard housed C58 mice in reversal learning. Thus, C58 mice exhibit behaviors that reflect both repetitive sensory motor behaviors as well as behavior that reflects resistance to change. Moreover, both clusters of repetitive behavior were attenuated by environmental enrichment. Such findings, along with the reported social deficits in C58 mice, increase the translational value of this mouse model to autism. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Storytelling and Repetitive Narratives for Design Empathy

    DEFF Research Database (Denmark)

    Fritsch, Jonas; Judice, Andrea; Soini, Katja

    2007-01-01

    Today it is widely established in design research that empathy is an important part of creating a true understanding of user experience as a resource for design. A typical challenge is how to transmit the feeling of empathy acquired by user studies to designers who have not participated in the user...... study. In this paper, we show how we attained an empathic understanding through storytelling and aroused empathy to others using repetitive narratives in an experimental presentation bringing forth factual, reflective and experiential aspects of the user information. Taking as a starting point our...... experiences with the design project Suomenlinna Seclusive, we conclude with the potential of using narratives for invoking design empathy....

  7. A miniature high repetition rate shock tube.

    Science.gov (United States)

    Tranter, R S; Lynch, P T

    2013-09-01

    A miniature high repetition rate shock tube with excellent reproducibility has been constructed to facilitate high temperature, high pressure, gas phase experiments at facilities such as synchrotron light sources where space is limited and many experiments need to be averaged to obtain adequate signal levels. The shock tube is designed to generate reaction conditions of T > 600 K, P shock waves with predictable characteristics are created, repeatably. Two synchrotron-based experiments using this apparatus are also briefly described here, demonstrating the potential of the shock tube for research at synchrotron light sources.

  8. Storytelling and Repetitive Narratives for Design Empathy

    DEFF Research Database (Denmark)

    Fritsch, Jonas; Judice, Andrea; Soini, Katja

    2007-01-01

    Today it is widely established in design research that empathy is an important part of creating a true understanding of user experience as a resource for design. A typical challenge is how to transmit the feeling of empathy acquired by user studies to designers who have not participated in the user...... study. In this paper, we show how we attained an empathic understanding through storytelling and aroused empathy to others using repetitive narratives in an experimental presentation bringing forth factual, reflective and experiential aspects of the user information. Taking as a starting point our...... experiences with the design project Suomenlinna Seclusive, we conclude with the potential of using narratives for invoking design empathy....

  9. The repetitive component of the sunflower genome

    Directory of Open Access Journals (Sweden)

    T. Giordani

    2014-08-01

    Full Text Available The sunflower (Helianthus annuus and species belonging to the genus Helianthus are emerging as a model species and genus for a number of studies on genome evolution. In this review, we report on the repetitive component of the H. annuus genome at the biochemical, molecular, cytological, and genomic levels. Recent work on sunflower genome composition is described, with emphasis on different types of repeat sequences, especially LTR-retrotransposons, of which we report on isolation, characterisation, cytological localisation, transcription, dynamics of proliferation, and comparative analyses within the genus Helianthus.

  10. Fixed latency on-chip interconnect for hardware spiking neural network architectures

    NARCIS (Netherlands)

    Pande, Sandeep; Morgan, Fearghal; Smit, Gerard; Bruintjes, Tom; Rutgers, Jochem; Cawley, Seamus; Harkin, Jim; McDaid, Liam

    2013-01-01

    Information in a Spiking Neural Network (SNN) is encoded as the relative timing between spikes. Distortion in spike timings can impact the accuracy of SNN operation by modifying the precise firing time of neurons within the SNN. Maintaining the integrity of spike timings is crucial for reliable oper

  11. Dual roles for spike signaling in cortical neural populations

    Directory of Open Access Journals (Sweden)

    Dana eBallard

    2011-06-01

    Full Text Available A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning post-stimulus histograms and exponential interval histograms. In addition it makes testable predictions that follow from the γ latency coding.

  12. Millisecond Precision Spike Timing Shapes Tactile Perception

    OpenAIRE

    Mackevicius, Emily L.; Best, Matthew D.; Hannes P Saal; Bensmaia, Sliman J.

    2012-01-01

    In primates, the sense of touch has traditionally been considered to be a spatial modality, drawing an analogy to the visual system. In this view, stimuli are encoded in spatial patterns of activity over the sheet of receptors embedded in the skin. We propose that the spatial processing mode is complemented by a temporal one. Indeed, the transduction and processing of complex, high-frequency skin vibrations have been shown to play an important role in tactile texture perception, and the frequ...

  13. Dissecting the functional anatomy of auditory word repetition

    Directory of Open Access Journals (Sweden)

    Thomas Matthew Hadley Hope

    2014-05-01

    Full Text Available Auditory word repetition involves many different brain regions, whose functions are still far from fully understood. Here, we use a single, multi-factorial, within-subjects fMRI design to identify those regions, and to functionally distinguish the multiple linguistic and non-linguistic processing areas that are all involved in repeating back heard words. The study compared: (1 auditory to visual inputs; (2 phonological to non-phonological inputs; (3 semantic to non-semantic inputs; and (4 speech production to finger-press responses. The stimuli included words (semantic and phonological inputs, pseudowords (phonological input, pictures and sounds of animals or objects (semantic input, and coloured patterns and hums (non-semantic and non-phonological. The speech production tasks involved auditory repetition, reading and naming while the finger press tasks involved one-back matching.The results from the main effects and interactions were compared to predictions from a previously reported functional anatomical model of language based on a meta-analysis of many different neuroimaging experiments. Although many findings from the current experiment replicated those predicted, our within-subject design also revealed novel results by providing sufficient anatomical precision to distinguish several different regions within: (1 the anterior insula (a dorsal region involved in both covert and overt speech production, and a more ventral region involved in overt speech only; (2 the pars orbitalis (with distinct sub-regions responding to phonological and semantic processing; (3 the anterior cingulate and SMA (whose subregions show differential sensitivity to speech and finger press responses; and (4 the cerebellum (with distinct regions for semantic processing, speech production and domain general processing. We also dissociated four different types of phonological effects in, respectively, the left superior temporal sulcus, left putamen, left ventral premoto

  14. Thalamic lesion and epilepsy with generalized seizures, ESES and spike-wave paroxysms--report of three cases.

    Science.gov (United States)

    Kelemen, Anna; Barsi, Péter; Gyorsok, Zsuzsanna; Sarac, Judit; Szucs, Anna; Halász, Péter

    2006-09-01

    We report three patients, who have thalamic lesion and secondary generalized epilepsy with generalized spike wave pattern. The first two patients have unilateral perinatal lesion, one with generalized tonic-clonic seizures on awakening the other with Landau-Kleffner-like syndrome. During the course of the disease both children developed electrical status epilepticus in slow wave sleep (ESES). The third patient has a dominantly unilateral thalamic tumor and epilepsy that mimics juvenile myoclonic epilepsy. All the patients have a lesion located in the inferior-medial-posterior part of the thalamus. The role of some thalamic and subthalamic nuclei in the generalized spike-wave electrical pattern patophysiology is discussed, with emphasis on the possible role of the inhibitory system from the zona incerta.

  15. Short-Range Temporal Interactions in Sleep; Hippocampal Spike Avalanches Support a Large Milieu of Sequential Activity Including Replay.

    Directory of Open Access Journals (Sweden)

    J Matthew Mahoney

    Full Text Available Hippocampal neural systems consolidate multiple complex behaviors into memory. However, the temporal structure of neural firing supporting complex memory consolidation is unknown. Replay of hippocampal place cells during sleep supports the view that a simple repetitive behavior modifies sleep firing dynamics, but does not explain how multiple episodes could be integrated into associative networks for recollection during future cognition. Here we decode sequential firing structure within spike avalanches of all pyramidal cells recorded in sleeping rats after running in a circular track. We find that short sequences that combine into multiple long sequences capture the majority of the sequential structure during sleep, including replay of hippocampal place cells. The ensemble, however, is not optimized for maximally producing the behavior-enriched episode. Thus behavioral programming of sequential correlations occurs at the level of short-range interactions, not whole behavioral sequences and these short sequences are assembled into a large and complex milieu that could support complex memory consolidation.

  16. Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons.

    Science.gov (United States)

    Sidiropoulou, Kyriaki; Poirazi, Panayiota

    2012-01-01

    Proper functioning of working memory involves the expression of stimulus-selective persistent activity in pyramidal neurons of the prefrontal cortex (PFC), which refers to neural activity that persists for seconds beyond the end of the stimulus. The mechanisms which PFC pyramidal neurons use to discriminate between preferred vs. neutral inputs at the cellular level are largely unknown. Moreover, the presence of pyramidal cell subtypes with different firing patterns, such as regular spiking and intrinsic bursting, raises the question as to what their distinct role might be in persistent firing in the PFC. Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. We identify synaptic location within the basal dendrites as a feature of stimulus selectivity. Specifically, persistent activity-inducing stimuli consist of activated synapses that are located more distally from the soma compared to non-inducing stimuli, in both model cells. In addition, the action potential (AP) latency and the first few inter-spike-intervals of the neuronal response can be used to reliably detect inducing vs. non-inducing inputs, suggesting a potential mechanism by which downstream neurons can rapidly decode the upcoming emergence of persistent activity. While the two model neurons did not differ in the coding features of persistent activity emergence, the properties of persistent activity, such as the firing pattern and the duration of temporally-restricted persistent activity were distinct. Collectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code

  17. Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme.

    Science.gov (United States)

    Masquelier, Timothée; Hugues, Etienne; Deco, Gustavo; Thorpe, Simon J

    2009-10-28

    Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential-a phenomenon referred to as "phase-of-firing coding" (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions-only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents ( approximately 10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.

  18. Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons.

    Directory of Open Access Journals (Sweden)

    Kyriaki Sidiropoulou

    Full Text Available Proper functioning of working memory involves the expression of stimulus-selective persistent activity in pyramidal neurons of the prefrontal cortex (PFC, which refers to neural activity that persists for seconds beyond the end of the stimulus. The mechanisms which PFC pyramidal neurons use to discriminate between preferred vs. neutral inputs at the cellular level are largely unknown. Moreover, the presence of pyramidal cell subtypes with different firing patterns, such as regular spiking and intrinsic bursting, raises the question as to what their distinct role might be in persistent firing in the PFC. Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS and an intrinsic bursting (IB model neuron. We identify synaptic location within the basal dendrites as a feature of stimulus selectivity. Specifically, persistent activity-inducing stimuli consist of activated synapses that are located more distally from the soma compared to non-inducing stimuli, in both model cells. In addition, the action potential (AP latency and the first few inter-spike-intervals of the neuronal response can be used to reliably detect inducing vs. non-inducing inputs, suggesting a potential mechanism by which downstream neurons can rapidly decode the upcoming emergence of persistent activity. While the two model neurons did not differ in the coding features of persistent activity emergence, the properties of persistent activity, such as the firing pattern and the duration of temporally-restricted persistent activity were distinct. Collectively, our results pinpoint to specific features of the neuronal response to a given

  19. Decoding of the spike timing of primary afferents during voluntary arm movements in monkeys.

    Science.gov (United States)

    Umeda, Tatsuya; Watanabe, Hidenori; Sato, Masa-Aki; Kawato, Mitsuo; Isa, Tadashi; Nishimura, Yukio

    2014-01-01

    Understanding the mechanisms of encoding forelimb kinematics in the activity of peripheral afferents is essential for developing a somatosensory neuroprosthesis. To investigate whether the spike timing of dorsal root ganglion (DRG) neurons could be estimated from the forelimb kinematics of behaving monkeys, we implanted two multi-electrode arrays chronically in the DRGs at the level of the cervical segments in two monkeys. Neuronal activity during voluntary reach-to-grasp movements were recorded simultaneously with the trajectories of hand/arm movements, which were tracked in three-dimensional space using a motion capture system. Sixteen and 13 neurons, including muscle spindles, skin receptors, and tendon organ afferents, were recorded in the two monkeys, respectively. We were able to reconstruct forelimb joint kinematics from the temporal firing pattern of a subset of DRG neurons using sparse linear regression (SLiR) analysis, suggesting that DRG neuronal ensembles encoded information about joint kinematics. Furthermore, we estimated the spike timing of the DRG neuronal ensembles from joint kinematics using an integrate-and-fire model (IF) incorporating the SLiR algorithm. The temporal change of firing frequency of a subpopulation of neurons was reconstructed precisely from forelimb kinematics using the SLiR. The estimated firing pattern of the DRG neuronal ensembles encoded forelimb joint angles and velocities as precisely as the originally recorded neuronal activity. These results suggest that a simple model can be used to generate an accurate estimate of the spike timing of DRG neuronal ensembles from forelimb joint kinematics, and is useful for designing a proprioceptive decoder in a brain machine interface.

  20. Unbiased estimation of precise temporal correlations between spike trains.

    Science.gov (United States)

    Stark, Eran; Abeles, Moshe

    2009-04-30

    A key issue in systems neuroscience is the contribution of precise temporal inter-neuronal interactions to information processing in the brain, and the main analytical tool used for studying pair-wise interactions is the cross-correlation histogram (CCH). Although simple to generate, a CCH is influenced by multiple factors in addition to precise temporal correlations between two spike trains, thus complicating its interpretation. A Monte-Carlo-based technique, the jittering method, has been suggested to isolate the contribution of precise temporal interactions to neural information processing. Here, we show that jittering spike trains is equivalent to convolving the CCH derived from the original trains with a finite window and using a Poisson distribution to estimate probabilities. Both procedures over-fit the original spike trains and therefore the resulting statistical tests are biased and have low power. We devise an alternative method, based on convolving the CCH with a partially hollowed window, and illustrate its utility using artificial and real spike trains. The modified convolution method is unbiased, has high power, and is computationally fast. We recommend caution in the use of the jittering method and in the interpretation of results based on it, and suggest using the modified convolution method for detecting precise temporal correlations between spike trains.

  1. Neuronal spike train entropy estimation by history clustering.

    Science.gov (United States)

    Watters, Nicholas; Reeke, George N

    2014-09-01

    Neurons send signals to each other by means of sequences of action potentials (spikes). Ignoring variations in spike amplitude and shape that are probably not meaningful to a receiving cell, the information content, or entropy of the signal depends on only the timing of action potentials, and because there is no external clock, only the interspike intervals, and not the absolute spike times, are significant. Estimating spike train entropy is a difficult task, particularly with small data sets, and many methods of entropy estimation have been proposed. Here we present two related model-based methods for estimating the entropy of neural signals and compare them to existing methods. One of the methods is fast and reasonably accurate, and it converges well with short spike time records; the other is impractically time-consuming but apparently very accurate, relying on generating artificial data that are a statistical match to the experimental data. Using the slow, accurate method to generate a best-estimate entropy value, we find that the faster estimator converges to this value more closely and with smaller data sets than many existing entropy estimators.

  2. Financial time series prediction using spiking neural networks.

    Science.gov (United States)

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  3. Financial time series prediction using spiking neural networks.

    Directory of Open Access Journals (Sweden)

    David Reid

    Full Text Available In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  4. Effects of nicotine stimulation on spikes, theta frequency oscillations, and spike-theta oscillation relationship in rat medial septum diagonal band Broca slices

    Institute of Scientific and Technical Information of China (English)

    Dong WEN; Ce PENG; Gao-xiang OU-YANG; Zainab HENDERSON; Xiao-li LI; Cheng-biao LU

    2013-01-01

    Aim:Spiking activities and neuronal network oscillations in the theta frequency range have been found in many cortical areas during information processing.The aim of this study is to determine whether nicotinic acetylcholine receptors (nAChRs) mediate neuronal network activity in rat medial septum diagonal band Broca (MSDB) slices.Methods:Extracellular field potentials were recorded in the slices using an Axoprobe 1A amplifier.Data analysis was performed offline.Spike sorting and local field potential (LFP) analyses were performed using Spike2 software.The role of spiking activity in the generation of LFP oscillations in the slices was determined by analyzing the phase-time relationship between the spikes and LFP oscillations.Circular statistic analysis based on the Rayleigh test was used to determine the significance of phase relationships between the spikes and LFP oscillations.The timing relationship was examined by quantifying the spike-field coherence (SFC).Results:Application of nicotine (250 nmol/L) induced prominent LFP oscillations in the theta frequency band and both small-and large-amplitude population spiking activity in the slices.These spikes were phase-locked to theta oscillations at specific phases.The Rayleigh test showed a statistically significant relationship in phase-locking between the spikes and theta oscillations.Larger changes in the SFC were observed for large-amplitude spikes,indicating an accurate timing relationship between this type of spike and LFP oscillations.The nicotine-induced spiking activity (large-amplitude population spikes) was suppressed by the nAChR antagonist dihydro-β-erythroidine (0.3 μmol/L).Conclusion:The results demonstrate that large-amplitude spikes are phase-locked to theta oscillations and have a high spike-timing accuracy,which are likely a main contributor to the theta oscillations generated in MSDB during nicotine receptor activation.

  5. Identification of two new repetitive elements and chromosomal mapping of repetitive DNA sequences in the fish Gymnothorax unicolor (Anguilliformes: Muraenidae

    Directory of Open Access Journals (Sweden)

    E. Coluccia

    2011-05-01

    Full Text Available Muraenidae is a species-rich family, with relationships among genera and species and taxonomy that have not been completely clarified. Few cytogenetic studies have been conducted on this family, and all of them showed the same diploid chromosome number (2n=42 but with conspicuous karyotypic variation among species. The Mediterranean moray eel Gymnothorax unicolor was previously cytogenetically studied using classical techniques that allowed the characterization of its karyotype structure and the constitutive heterochromatin and argyrophilic nucleolar organizer regions (Ag-NORs distribution pattern. In the present study, we describe two new repetitive elements (called GuMboI and GuDdeI obtained from restricted genomic DNA of G. unicolor that were characterized by Southern blot and physically localized by in situ hybridization on metaphase chromosomes. As they are highly repetitive DNA sequences, they map in heterochromatic regions. However, while GuDdeI was localized in the centromeric regions, the GuMboI fraction was distributed on some centromeres and was co-localized with the nucleolus organizer region (NOR. Comparative analysis with other Mediterranean species such as Muraena helena pointed out that these DNA fractions are species-specific and could potentially be used for species discrimination. As a new contribution to the karyotype of this species, we found that the major ribosomal genes are localized on acrocentric chromosome 9 and that the telomeres of each chromosome are composed of a tandem repeat derived from a poly-TTAGGG DNA sequence, as it occurs in most vertebrate species. The results obtained add new information useful in comparative genomics at the chromosomal level and contribute to the cytogenetic knowledge regarding this fish family, which has not been extensively studied.

  6. Point process modeling and estimation: Advances in the analysis of dynamic neural spiking data

    Science.gov (United States)

    Deng, Xinyi

    A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in

  7. The role of short-term memory impairment in nonword repetition, real word repetition, and nonword decoding: A case study.

    Science.gov (United States)

    Peter, Beate

    2017-09-21

    In a companion study, adults with dyslexia and adults with a probable history of childhood apraxia of speech showed evidence of difficulty with processing sequential information during nonword repetition, multisyllabic real word repetition and nonword decoding. Results suggested that some errors arose in visual encoding during nonword reading, all levels of processing but especially short-term memory storage/retrieval during nonword repetition, and motor planning and programming during complex real word repetition. To further investigate the role of short-term memory, a participant with short-term memory impairment (MI) was recruited. MI was confirmed with poor performance during a sentence repetition and three nonword repetition tasks, all of which have a high short-term memory load, whereas typical performance was observed during tests of reading, spelling, and static verbal knowledge, all with low short-term memory loads. Experimental results show error-free performance during multisyllabic real word repetition but high counts of sequence errors, especially migrations and assimilations, during nonword repetition, supporting short-term memory as a locus of sequential processing deficit during nonword repetition. Results are also consistent with the hypothesis that during complex real word repetition, short-term memory is bypassed as the word is recognized and retrieved from long-term memory prior to producing the word.

  8. Repetition and Reactance in Graham’s "Underneath" Poems

    Directory of Open Access Journals (Sweden)

    Roghayeh Farsi

    2017-09-01

    Full Text Available The present paper gives a detailed analysis and interpretation of 16 poems in Jorie Graham's collection, Swarm (2000, which bear "UNDERNEATH" as their main titles. The poems are marked with different types of repetition such as graphological repetition, word, phrase, and sentential repetition, semantic repetition, and syntactic repetition. The study draws on Lakoff and Johnson's theories on metaphor and Brehm and Brehm’s reactance theory. It is argued "underneath" is a conceptual (orientational metaphor which signifies a state of being limited, lack of control and freedom, and loss of power. The paper investigates the speaker's reactant behavior in "Underneath" poems, seeking a way to restore her lost freedom. Reactance behaviors can be skepticism, inertia, aggression, and resistance. It is concluded despite her thematic inertia, representing her submission to the oppressed state, her stylistic reactance reflected in repetitions, innovations, and disruptive diction stands for her attempts to regain her lost control.

  9. Optimal Control of Laser-Plasma Instabilities Using Spike Trains of Uneven Duration and Delay: STUD Pulses

    CERN Document Server

    Afeyan, Bedros

    2013-01-01

    Adaptive methods of laser irradiation of plasmas are proposed consisting of deterministic, `on-off' amplitude modulations in time, and intermittently changing speckle-patterns. These laser pulses consist of a series of picosecond time-scale spikes in a spike train of uneven duration and delay (STUD pulses), in contrast to hydrodynamic-time-scale modulated, multi-nanosecond pulses for laser fusion. Properly designed STUD pulses minimize backscatter and tame any absorptive parametric instability for a given set of plasma conditions, by adjusting the modulation periods, duty cycles and spatial hot-spot-distribution scrambling-rates of the spikes. Traditional methods of beam conditioning are subsumed or surpassed by STUD pulses. In addition, STUD pulses allow an advance in the control of instabilities driven by spatially overlapped laser beams by allowing the spikes of crossing beams to be temporally staggered. When the intensity peaks of one fall within the nulls of its crossing beam, it allows an on-off switch ...

  10. Point-process analysis of neural spiking activity of muscle spindles recorded from thin-film longitudinal intrafascicular electrodes.

    Science.gov (United States)

    Citi, Luca; Djilas, Milan; Azevedo-Coste, Christine; Yoshida, Ken; Brown, Emery N; Barbieri, Riccardo

    2011-01-01

    Recordings from thin-film Longitudinal Intra-Fascicular Electrodes (tfLIFE) together with a wavelet-based de-noising and a correlation-based spike sorting algorithm, give access to firing patterns of muscle spindle afferents. In this study we use a point process probability structure to assess mechanical stimulus-response characteristics of muscle spindle spike trains. We assume that the stimulus intensity is primarily a linear combination of the spontaneous firing rate, the muscle extension, and the stretch velocity. By using the ability of the point process framework to provide an objective goodness of fit analysis, we were able to distinguish two classes of spike clusters with different statistical structure. We found that spike clusters with higher SNR have a temporal structure that can be fitted by an inverse Gaussian distribution while lower SNR clusters follow a Poisson-like distribution. The point process algorithm is further able to provide the instantaneous intensity function associated with the stimulus-response model with the best goodness of fit. This important result is a first step towards a point process decoding algorithm to estimate the muscle length and possibly provide closed loop Functional Electrical Stimulation (FES) systems with natural sensory feedback information.

  11. Molecular cloning, characterization and expression of WAG-2 alternative splicing transcripts in developing spikes of Aegilops tauschii

    Indian Academy of Sciences (India)

    SHUHONG WEI

    2016-09-01

    WAG-2 is a C-class MADS-box gene, which is orthologous to AGAMOUS (AG )inArabidopsis. The AG group C-classMADS-box genes are involved in stamen and pistil identity. In this study, two WAG-2 transcripts, namely, WAG-2f and WAG-2g, were isolated and characterized from Aegilops tauschii . The open reading frames of WAG-2f and WAG-2g were 825 and 822 bp, respectively, encoding 275 and 274 amino acid residues. BLAST searches of partial WAG-2 genomic sequence againstthe draft sequence of Ae. tauschii genome database revealed the complex structure of WAG-2 gene, which consisted of seven exons and six introns. TheWAG-2f and WAG-2g cDNAs were two alternative splicing transcripts. The alternative splicing events were produced by an alternative 5 ' splice site. The expression level of WAG-2f transcript, which was extremely weak inyoung spikes of floret primordium formation stage, increased as the spikes developed. The highest expression was observed in the spikes at the anther separation stage. Low expression levels of WAG-2f were also detected at the tetrad stage. The WAG-2g transcript was expressed at all four stages of spike development but at a relatively low level. The expression pattern of thetwo transcripts was distinctly different during floral development, thereby suggesting a functional divergence.

  12. Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

    Science.gov (United States)

    Erfanian Saeedi, Nafise; Blamey, Peter J; Burkitt, Anthony N; Grayden, David B

    2016-04-01

    Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

  13. Genetic threshold hypothesis of neocortical spike-and-wave discharges in the rat: an animal model of petit mal epilepsy.

    Science.gov (United States)

    Vadász, C; Carpi, D; Jando, G; Kandel, A; Urioste, R; Horváth, Z; Pierre, E; Vadi, D; Fleischer, A; Buzsáki, G

    1995-02-27

    Neocortical high-voltage spike-and-wave discharges (HVS) in the rat are an animal model of petit mal epilepsy. Genetic analysis of total duration of HVS (s/12 hr) in reciprocal F1 and F2 hybrids of F344 and BN rats indicated that the phenotypic variability of HVS cannot be explained by a simple, monogenic Mendelian model. Biometrical analysis suggested the presence of additive, dominance, and sex-linked-epistatic effects, buffering maternal influence, and heterosis. High correlation was observed between average duration (s/episode) and frequency of occurrence of spike-and-wave episodes (n/12 hr) in parental and segregating generations, indicating that common genes affect both duration and frequency of the spike-and-wave pattern. We propose that both genetic and developmental-environmental factors control an underlying quantitative variable, which, above a certain threshold level, precipitates HVS discharges. These findings, together with the recent availability of rat DNA markers for total genome mapping, pave the way to the identification of genes that control the susceptibility of the brain to spike-and-wave discharges.

  14. Genetic threshold hypothesis of neocortical spike-and-wave discharges in the rat: An animal model of petit mal epilepsy

    Energy Technology Data Exchange (ETDEWEB)

    Vadasz, C.; Fleischer, A. [Nathan Kline Inst. for Psychiatric Research, Orangeburg, NY (United States); Carpi, D.; Jando, G. [State Univ. of New Jersey, Newark, NJ (United States)] [and others

    1995-02-27

    Neocortical high-voltage spike-and-wave discharges (HVS) in the rat are an animal model of petit mal epilepsy. Genetic analysis of total duration of HVS (s/12 hr) in reciprocal F1 and F2 hybrids of F344 and BN rats indicated that the phenotypic variability of HVS cannot be explained by simple, monogenic Mendelian model. Biometrical analysis suggested the presence of additive, dominance, and sex-linked-epistatic effects, buffering maternal influence, and heterosis. High correlation was observed between average duration (s/episode) and frequency of occurrence of spike-and-wave episodes (n/12 hr) in parental and segregating generations, indicating that common genes affect both duration and frequency of the spike-and-wave pattern. We propose that both genetic and developmental - environmental factors control an underlying quantitative variable, which, above a certain threshold level, precipitates HVS discharges. These findings, together with the recent availability of rat DNA markers for total genome mapping, pave the way to the identification of genes that control the susceptibility of the brain to spike-and-wave discharges. 67 refs., 3 figs., 5 tabs.

  15. Radioxenon detector calibration spike production and delivery systems

    Energy Technology Data Exchange (ETDEWEB)

    Foxe, Michael P.; Cameron, Ian M.; Cooper, Matthew W.; Haas, Derek A.; Hayes, James C.; Kriss, Aaron A.; Lidey, Lance S.; Mendez, Jennifer M.; Prinke, Amanda M.; Riedmann, Robin A.

    2016-03-01

    Abstract Beta-Gamma coincidence radioxenon detectors must be calibrated for each of the four-radioxenon isotopes (135Xe, 133Xe, 133mXe, and 131mXe). Without a proper calibration, there is potential for the misidentification of the amount of each isotope detected. It is important to accurately determine the amount of each radioxenon isotope, as the ratios can be used to distinguish between an anthropogenic source and a nuclear explosion. We have developed a xenon calibration system (XeCalS) that produces calibration spikes of known activity and pressure for field calibration of detectors. The activity concentrations of these calibration spikes are measured using a beta-gamma coincidence detector and a high purity germanium (HPGe) detector. We will present the results from the development and commissioning of XeCalS, along with the future plans for a portable spike implementation system.

  16. Grain price spikes and beggar-thy-neighbor policy responses

    DEFF Research Database (Denmark)

    Boysen, Ole; Jensen, Hans Grinsted

    Upward spikes in the international price of food in recent years led some countries to raise export barriers, thereby exacerbating both the price spike and reducing the terms of trade for food-importing countries (beggaring their neighbors). At the same time, and for similar political......-economy reasons, numerous food-importing countries reduced or suspended their import tariffs, and some even provided food import subsidies -- which also exacerbated the international price spike, thus turning the terms of trade even further against food-importing countries. This issue became a major item...... on the agenda of various international policy fora, including the annual meetings of G20 countries in recent years. For that reason, recent studies have attempted to quantify the extent to which such policy actions contributed to the rise in food prices. A study by Jensen & Anderson (2014) uses the global AGE...

  17. The thermodynamic temperature of a rhythmic spiking network

    CERN Document Server

    Merolla, Paul; Arthur, John

    2010-01-01

    Artificial neural networks built from two-state neurons are powerful computational substrates, whose computational ability is well understood by analogy with statistical mechanics. In this work, we introduce similar analogies in the context of spiking neurons in a fixed time window, where excitatory and inhibitory inputs drawn from a Poisson distribution play the role of temperature. For single neurons with a "bandgap" between their inputs and the spike threshold, this temperature allows for stochastic spiking. By imposing a global inhibitory rhythm over the fixed time windows, we connect neurons into a network that exhibits synchronous, clock-like updating akin to neural networks. We implement a single-layer Boltzmann machine without learning to demonstrate our model.

  18. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

    Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

  19. Stable propagation of synchronous spiking in cortical neural networks

    Science.gov (United States)

    Diesmann, Markus; Gewaltig, Marc-Oliver; Aertsen, Ad

    1999-12-01

    The classical view of neural coding has emphasized the importance of information carried by the rate at which neurons discharge action potentials. More recent proposals that information may be carried by precise spike timing have been challenged by the assumption that these neurons operate in a noisy fashion-presumably reflecting fluctuations in synaptic input-and, thus, incapable of transmitting signals with millisecond fidelity. Here we show that precisely synchronized action potentials can propagate within a model of cortical network activity that recapitulates many of the features of biological systems. An attractor, yielding a stable spiking precision in the (sub)millisecond range, governs the dynamics of synchronization. Our results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network.

  20. Entropy-based parametric estimation of spike train statistics

    CERN Document Server

    Vasquez, J C; Viéville, T

    2010-01-01

    We consider the evolution of a network of neurons, focusing on the asymptotic behavior of spikes dynamics instead of membrane potential dynamics. The spike response is not sought as a deterministic response in this context, but as a conditional probability : "Reading out the code" consists of inferring such a probability. This probability is computed from empirical raster plots, by using the framework of thermodynamic formalism in ergodic theory. This gives us a parametric statistical model where the probability has the form of a Gibbs distribution. In this respect, this approach generalizes the seminal and profound work of Schneidman and collaborators. A minimal presentation of the formalism is reviewed here, while a general algorithmic estimation method is proposed yielding fast convergent implementations. It is also made explicit how several spike observables (entropy, rate, synchronizations, correlations) are given in closed-form from the parametric estimation. This paradigm does not only allow us to esti...